U.S. patent application number 17/230700 was filed with the patent office on 2021-07-29 for selectively conditioning audio signals.
This patent application is currently assigned to Orcam Vision Technologies Ltd.. The applicant listed for this patent is Orcam Vision Technologies Ltd.. Invention is credited to Amnon Shashua, Yonatan WEXLER.
Application Number | 20210235207 17/230700 |
Document ID | / |
Family ID | 1000005569551 |
Filed Date | 2021-07-29 |
United States Patent
Application |
20210235207 |
Kind Code |
A1 |
WEXLER; Yonatan ; et
al. |
July 29, 2021 |
SELECTIVELY CONDITIONING AUDIO SIGNALS
Abstract
A system may include a wearable camera configured to capture
images and a microphone configured to capture sounds. The system
may also include a processor programmed to receive the images;
identify a representation of an individual in at least one of the
images; determine whether the individual is a recognized
individual; if the individual is determined to be a recognized
individual, cause an image of the individual to be shown on a
display and selectively condition at least one audio signal and
determined to be associated with the recognized individual; and
cause transmission of the at least one conditioned audio signal to
a hearing interface device configured to provide sound to an ear of
a user.
Inventors: |
WEXLER; Yonatan; (Jerusalem,
IL) ; Shashua; Amnon; (Mevaseret Zion, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Orcam Vision Technologies Ltd. |
Jerusalem |
|
IL |
|
|
Assignee: |
Orcam Vision Technologies
Ltd.
Jerusalem
IL
|
Family ID: |
1000005569551 |
Appl. No.: |
17/230700 |
Filed: |
April 14, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/IB2019/001132 |
Oct 10, 2019 |
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17230700 |
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62745478 |
Oct 15, 2018 |
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62746595 |
Oct 17, 2018 |
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62808317 |
Feb 21, 2019 |
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62857773 |
Jun 5, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00281 20130101;
H04N 5/2252 20130101; H04R 25/407 20130101; H04R 2225/025 20130101;
H04R 2225/55 20130101; G06K 9/00362 20130101; H04R 25/606 20130101;
G06K 9/00288 20130101; H04R 25/505 20130101; G10L 17/06 20130101;
G06K 9/00664 20130101; G06K 9/6217 20130101; H04R 25/558 20130101;
H04R 25/405 20130101; H04R 1/08 20130101 |
International
Class: |
H04R 25/00 20060101
H04R025/00; G06K 9/00 20060101 G06K009/00; H04R 1/08 20060101
H04R001/08; G06K 9/62 20060101 G06K009/62; G10L 17/06 20060101
G10L017/06; H04N 5/225 20060101 H04N005/225 |
Claims
1-150. (canceled)
151. A hearing aid system comprising: a wearable camera configured
to capture a plurality of images from an environment of a user; at
least one microphone configured to capture sounds from an
environment of the user; and at least one processor programmed to:
receive the plurality of images captured by the camera; identify a
representation of at least one individual in at least one of the
plurality of images; determine whether the at least one individual
is a recognized individual; if the at least one individual is
determined to be a recognized individual, cause an image of the at
least one individual to be shown on a display and selectively
condition at least one audio signal that is received from the at
least one microphone and determined to be associated with the
recognized individual; and cause transmission of the at least one
conditioned audio signal to a hearing interface device configured
to provide sound to an ear of the user.
152. The system of claim 151, wherein the determination of whether
the at least one individual is a recognized individual is based on
one or more facial features associated with the at least one
individual that is detected based on analysis of the at least one
of the plurality of images.
153. The system of claim 152, wherein the one or more detected
facial features are compared to a database storing facial feature
information associated with recognized individuals.
154. The system of claim 151, wherein the determination of whether
the at least one individual is a recognized individual is based on
an output of a trained neural network supplied with the at least
one of the plurality of images.
155. The system of claim 151, wherein the determination of whether
the at least one individual is a recognized individual is based on
analysis of the at least one audio signal received from the
microphone and detection in the at least one audio signal of one or
more predetermined voiceprint characteristics associated with the
recognized individual.
156. The system of claim 151, wherein the display is included on a
housing common to the wearable camera and the at least one
microphone.
157. The system of claim 151, wherein the display is included on a
paired mobile device.
158. The system of claim 151, wherein the selective conditioning
includes attenuating the at least one audio signal.
159. The system of claim 151, wherein the selective conditioning
includes amplification of the at least one audio signal.
160. The system of claim 151, wherein the selective conditioning
includes changing a tone associated with the at least one audio
signal.
161. The system of claim 151, wherein the selective conditioning
includes changing a rate of speech associated with the at least one
audio signal.
162. The system of claim 151, wherein the at least one audio signal
is determined to be associated with the recognized individual based
on a detected look direction for the user, determined based on a
direction associated with a chin of the user detected in the at
least one of the plurality of images.
163. The system of claim 151, wherein the at least one audio signal
is determined to be associated with the recognized individual based
on lip movements of the user, as detected based on analysis of the
plurality of images.
164. The system of claim 163, further based on a determination of
whether the detected lip movements are consistent with a voice
signal associated with the at least one audio signal.
165. The system of claim 151, wherein the at least one audio signal
is determined to be associated with the recognized individual based
on one or more predetermined voiceprint characteristics associated
with the recognized individual detected in the at least one audio
signal.
166. The system of claim 151, wherein the image of the at least one
individual caused to be shown on the display is retrieved from a
database stored in memory, the database associating recognized
individuals with corresponding images.
167. The system of claim 151, wherein the image of the at least one
individual caused to be shown on the display is derived from the at
least one image.
168. The system of claim 151, wherein the wearable camera and the
at least one microphone are included in a common housing.
169. The system of claim 168, wherein the at least one processor is
included in the common housing.
170. The system of claim 168, wherein the at least one processor is
included in a second housing separate from the common housing.
171. The system of claim 170, wherein the at least one processor is
configured to receive the captured images via a wireless link
between a transmitter in the common housing and receiver in the
second housing.
172. The system of claim 170, wherein the second housing is
associated with a paired mobile device.
173. The system of claim 151, wherein the at least one microphone
includes a directional microphone.
174. The system of claim 151, wherein the at least one microphone
includes a microphone array.
175. The system of claim 151, herein the hearing interface device
includes a speaker associated with an earpiece.
176. The system of claim 151, wherein the hearing interface device
includes a bone conduction microphone.
177. The system of claim 151, wherein the at least one processor is
further programmed to store in a database information relating to
encounters with individuals.
178. The system of claim 177, wherein the database is configured to
track encounters with individuals chronologically.
179. The system of claim 177, wherein, based on input received from
the user, the at least one processor is configured to forego
storage in the database of information relating to encounters with
one or more particular individuals identified in the plurality of
images.
180. The system of claim 177, wherein access to the database
storing information relating to encounters with individuals is
password protected.
181. The system of claim 177, wherein the at least one processor is
configured to forego storage in the database of information
relating to encounters with one or more particular individuals
determined to be associated with one or more predetermined
groups.
182. The system of claim 177, wherein the one or more predetermined
groups include at least one of office workers, service personnel,
or persons to which no vocal interaction is directed from the
user.
183. A hearing aid system comprising: a wearable camera configured
to capture a plurality of images from an environment of a user; at
least one microphone configured to capture sounds from an
environment of the user; and at least one processor programmed to:
receive an audio signal from the at least one microphone; determine
whether the received audio signal is associated with a recognized
individual; if the at least one individual is determined to be a
recognized individual, cause an image of the at least one
individual to be shown on a display and selectively condition the
audio signal; and cause transmission of the conditioned audio
signal to a hearing interface device configured to provide sound to
an ear of the user.
184. The system of claim 183, wherein the determination of whether
the received audio signal is associated with a recognized
individual is based on a determination that an individual detected
based on analysis of one or more of the plurality of images
captured by the camera is associated with the audio signal.
185. The system of claim 184, further based on one or more detected
facial features associated with the detected individual that is
compared to a database storing facial feature information
associated with recognized individuals.
186. The system of claim 183, wherein the determination of whether
the received audio signal is associated with a recognized
individual is based on an output of a trained neural network.
187. The system of claim 183, wherein the determination of whether
the at least one individual is a recognized individual is based on
analysis of the audio signal received from the microphone and
detection in the audio signal of one or more predetermined
voiceprint characteristics associated with the recognized
individual.
188. The system of claim 183, wherein the display is included on a
housing common to the wearable camera and the at least one
microphone.
189. The system of claim 183, wherein the display is included on a
paired mobile device.
190. The system of claim 183, wherein the selective conditioning
includes attenuating the at least one audio signal.
191. The system of claim 183, wherein the selective conditioning
includes amplification of the at least one audio signal.
192. The system of claim 183, wherein the selective conditioning
includes changing a tone associated with the at least one audio
signal.
193. The system of claim 183, wherein the selective conditioning
includes changing a rate of speech associated with the at least one
audio signal.
194. The system of claim 183, wherein the wearable camera and the
at least one microphone are included in a common housing.
195. The system of claim 194, wherein the at least one processor is
included in the common housing.
196. The system of claim 183, wherein the at least one processor is
included in a second housing separate from the common housing.
197. The system of claim 196, wherein the at least one processor is
configured to receive the captured images via a wireless link
between a transmitter in the common housing and receiver in the
second housing.
198. The system of claim 196, wherein the second housing is
associated with a paired mobile device.
199. The system of claim 183, wherein the at least one microphone
includes a directional microphone.
200. The system of claim 183, wherein the at least one microphone
includes a microphone array.
201. The system of claim 183, wherein the hearing interface device
includes a speaker associated with an earpiece.
202. The system of claim 183, wherein the hearing interface device
includes a bone conduction microphone.
203-338. (canceled)
Description
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority of U.S.
Provisional patent Application No. 62/745,478, filed on Oct. 15,
2018; U.S. Provisional Patent Application No. 62/746,595, filed on
Oct. 17, 2018; U.S. Provisional Patent Application No. 62/808,317,
filed on Feb. 21, 2019; and U.S. Provisional Patent Application No.
62/857,773, filed on Jun. 5, 2019. All of the foregoing
applications are incorporated herein by reference in their
entirety.
BACKGROUND
Technical Field
[0002] This disclosure generally relates to devices and methods for
capturing and processing images and audio from an environment of a
user, and using information derived from captured images and
audio.
Background Information
[0003] Today, technological advancements make it possible for
wearable devices to automatically capture images and audio, and
store information that is associated with the captured images and
audio. Certain devices have been used to digitally record aspects
and personal experiences of one's life in an exercise typically
called "lifelogging." Some individuals log their life so they can
retrieve moments from past activities, for example, social events,
trips, etc. Lifelogging may also have significant benefits in other
fields (e.g., business, fitness and healthcare, and social
research). Lifelogging devices, while useful for tracking daily
activities, may be improved with capability to enhance one's
interaction in his environment with feedback and other advanced
functionality based on the analysis of captured image and audio
data.
[0004] Even though users can capture images and audio with their
smartphones and some smartphone applications can process the
captured information, smartphones may not be the best platform for
serving as lifelogging apparatuses in view of their size and
design. Lifelogging apparatuses should be small and light, so they
can be easily worn. Moreover, with improvements in image capture
devices, including wearable apparatuses, additional functionality
may be provided to assist users in navigating in and around an
environment, identifying persons and objects they encounter, and
providing feedback to the users about their surroundings and
activities. Therefore, there is a need for apparatuses and methods
for automatically capturing and processing images and audio to
provide useful information to users of the apparatuses, and for
systems and methods to process and leverage information gathered by
the apparatuses.
SUMMARY
[0005] Embodiments consistent with the present disclosure provide
devices and methods for automatically capturing and processing
images and audio from an environment of a user, and systems and
methods for processing information related to images and audio
captured from the environment of the user.
[0006] In an embodiment, a hearing aid system may selectively
amplify sounds emanating from a detected look direction of a user
of the hearing aid system. The system may include a wearable camera
configured to capture a plurality of images from an environment of
the user; at least one microphone configured to capture sounds from
an environment of the user; and at least one processor. The
processor may be programmed to receive the plurality of images
captured by the camera, receive audio signals representative of
sounds received by the at least one microphone from the environment
of the user, determine a look direction for the user based on
analysis of at least one of the plurality of images, cause
selective conditioning of at least one audio signal received by the
at least one microphone from a region associated with the look
direction of the user, and cause transmission of the at least one
conditioned audio signal to a hearing interface device configured
to provide sound to an ear of the user.
[0007] In an embodiment, a method may selectively amplify sounds
emanating from a detected look direction of a user of the hearing
aid system. The method may comprise receiving a plurality of images
captured by a wearable camera from an environment of a user;
receiving audio signals representative of sounds captured by at
least one microphone from the environment of the user, determining
a look direction for the user based on analysis of at least one of
the plurality of images, causing selective conditioning of at least
one audio signal received by the at least one microphone from a
region associated with the look direction of the user, causing
transmission of the at least one conditioned audio signal to a
hearing interface device configured to provide sound to an ear of
the user.
[0008] In an embodiment, a hearing aid system may selectively
amplify audio signals associated with a voice of a recognized
individual. The system may include a wearable camera configured to
capture a plurality of images from an environment of the user, at
least one microphone configured to capture sounds from an
environment of the user, and at least one processor. The processor
may be programmed to receive the plurality of images captured by
the camera, identify a representation of at least one recognized
individual in at least one of the plurality of images, receive
audio signals representative of the sounds captured by the at least
one microphone, cause selective conditioning of at least one audio
signal received by the at least one microphone from a region
associated with the at least one recognized individual, and cause
transmission of the at least one conditioned audio signal to a
hearing interface device configured to provide sound to an ear of
the user.
[0009] In an embodiment, a method may selectively amplify audio
signals associated with a voice of a recognized individual. The
method may comprise receiving a plurality of images captured by a
wearable camera from an environment of the user, identifying a
representation of at least one recognized individual in at least
one of the plurality of images, receiving audio signals
representative of sounds captured by at least one microphone from
the environment of the user, causing selective conditioning of at
least one audio signal received by the at least one microphone from
a region associated with the at least one recognized individual,
and causing transmission of the at least one conditioned audio
signal to a hearing interface device configured to provide sound to
an ear of the user.
[0010] In an embodiment, a voice transmission system may
selectively transmit audio signals associated with a voice of a
recognized user. The system may include at least one microphone
configured to capture sounds from an environment of the user and at
least one processor. The processor may be programmed to receive
audio signals representative of the sounds captured by the at least
one microphone, identify, based on analysis of the received audio
signals, one or more voice audio signals representative of a
recognized voice of the user, cause transmission, to a remotely
located device, of the one or more voice audio signals
representative of the recognized voice of the user, and prevent
transmission, to the remotely located device, of at least one
background noise audio signal different from the one or more voice
audio signals representative of a recognized voice of the user.
[0011] In an embodiment, a method may selectively transmit audio
signals associated with a voice of a recognized user. The method
may comprise receiving audio signals representative of sounds
captured by at least one microphone from an environment of a user,
identifying, based on analysis of the received audio signals, one
or more voice audio signals representative of a recognized voice of
the user, causing transmission, to a remotely located device, of
the one or more voice audio signals representative of the
recognized voice of the user, and preventing transmission, to the
remotely located device, of at least one background noise audio
signal different from the one or more voice audio signals
representative of a recognized voice of the user.
[0012] In an embodiment, a hearing aid system may selectively
amplify audio signals based on tracked lip movements. The system
may include a wearable camera configured to capture a plurality of
images from an environment of the user, at least one microphone
configured to capture sounds from an environment of the user, and
at least one processor. The processor may be programmed to receive
the plurality of images captured by the camera; identify a
representation of at least one individual in at least one of the
plurality of images; identify at least one lip movement associated
with a mouth of the individual, based on analysis of the plurality
of images; receive audio signals representative of the sounds
captured by the at least one microphone; identify, based on
analysis of the sounds captured by the at least one microphone, at
least a first audio signal associated with a first voice and at
least a second audio signal associated with a second voice
different from the first voice; cause selective conditioning of the
first audio signal based on a determination by the at least one
processor that the first audio signal is associated with the
identified at least one lip movement associated with the mouth of
the individual; and cause transmission of the selectively
conditioned first audio signal to a hearing interface device
configured to provide sound to an ear of the user.
[0013] In an embodiment, a method may selectively amplify audio
signals based on tracked lip movements. The method may comprise
receiving a plurality of images captured by a wearable camera from
an environment of the user; identifying a representation of at
least one individual in at least one of the plurality of images;
identifying at least one lip movement associated with a mouth of
the individual, based on analysis of the plurality of images;
receiving audio signals representative of the sounds captured by at
least one microphone from the environment of the user; identifying,
based on analysis of the sounds captured by the at least one
microphone, at least a first audio signal associated with a first
voice and at least a second audio signal associated with a second
voice different from the first voice; causing selective
conditioning of the first audio signal based on a determination by
the at least one processor that the first audio signal is
associated with the identified at least one lip movement associated
with the mouth of the individual; and causing transmission of the
selectively conditioned first audio signal to a hearing interface
device configured to provide sound to an ear of the user.
[0014] In an embodiment, a hearing aid system for amplifying audio
signals may comprise a wearable camera configured to capture a
plurality of images from an environment of a user and at least one
microphone configured to capture sounds from an environment of the
user. The hearing aid system may also include at least one
processor programmed to receive the plurality of images captured by
the camera and identify a representation of a first individual and
a representation of a second individual in the plurality of images.
The at least one processor may also be programmed to receive from
the at least one microphone a first audio signal associated with a
voice of the first individual and receive from the at least one
microphone a second audio signal associated with a voice of the
second individual. The at least one processor may further be
programmed to detect at least one amplification criteria indicative
of a voice amplification priority between the first individual and
the second individual. The at least one processor may also be
programmed to selectively amplify the first audio signal relative
to the second audio signal when the at least one amplification
criteria indicates that the first individual has voice
amplification priority over the second individual and selectively
amplify the second audio signal relative to the first audio signal
when the at least one amplification criteria indicates that the
second individual has voice amplification priority over the first
individual. The at least one processor may further be programmed to
cause transmission of the selectively amplified first or second
audio signal to a hearing interface device configured to provide
sound to an ear of the user.
[0015] In an embodiment, a computer-implemented method for
selectively amplifying audio signals may comprise receiving the
plurality of images captured by a camera from an environment of a
user and identifying a representation of a first individual and a
representation of a second individual in the plurality of images.
The method may also comprise receiving from at least one microphone
a first audio signal associated with a voice of the first
individual and receiving from the at least one microphone a second
audio signal associated with a voice of the second individual. The
method may further comprise detecting at least one amplification
criteria indicative of a voice amplification priority between the
first individual and the second individual. The method may also
comprise selectively amplifying the first audio signal relative to
the second audio signal when the at least one amplification
criteria indicates that the first individual has voice
amplification priority over the second individual and selectively
amplifying the second audio signal relative to the first audio
signal when the at least one amplification criteria indicates that
the second individual has voice amplification priority over the
first individual. The method may further comprise causing
transmission of the selectively amplified first or second audio
signal to a hearing interface device configured to provide sound to
an ear of the user.
[0016] In an embodiment, a non-transitory computer-readable medium
store instructions that, when executed by at least one processor,
may cause a device to perform a method comprising receiving the
plurality of images captured by a camera from an environment of a
user and identifying a representation of a first individual and a
representation of a second individual in the plurality of images.
The method may also comprise receiving from at least one microphone
a first audio signal associated with a voice of the first
individual and receiving from the at least one microphone a second
audio signal associated with a voice of the second individual. The
method may further comprise detecting at least one amplification
criteria indicative of a voice amplification priority between the
first individual and the second individual. The method may also
comprise selectively amplifying the first audio signal relative to
the second audio signal when the at least one amplification
criteria indicates that the first individual has voice
amplification priority over the second individual and selectively
amplifying the second audio signal relative to the first audio
signal when the at least one amplification criteria indicates that
the second individual has voice amplification priority over the
first individual. The method may further comprise causing
transmission of the selectively amplified first or second audio
signal to a hearing interface device configured to provide sound to
an ear of the user.
[0017] In an embodiment, a hearing aid system for selectively
amplifying audio signals may comprise a wearable camera configured
to capture a plurality of images from an environment of a user and
at least one microphone configured to capture sounds from an
environment of the user. The hearing aid system may also include at
least one processor programmed to receive the plurality of images
captured by the camera; identify a representation of one or more
individuals in the plurality of images; receive from the at least
one microphone a first audio signal associated with a voice;
determine, based on analysis of the plurality of images, that the
first audio signal is not associated with a voice of any of the one
or more individuals; receive from the at least one microphone a
second audio signal associated with a voice; determine, based on
analysis of the plurality of images, that the second audio signal
is associated with a voice of one of the one or more individuals;
cause a first amplification of the first audio signal and a second
amplification of the second audio signal, wherein the first
amplification differs from the second amplification in at least one
aspect; and cause transmission of at least one of the first audio
signal, amplified according to the first amplification, and the
second audio signal, amplified according to the second
amplification, to a hearing interface device configured to provide
sound to an ear of the user.
[0018] In an embodiment, a hearing aid system for selectively
amplifying audio signals may comprise a wearable camera configured
to capture a plurality of images from an environment of a user and
at least one microphone configured to capture sounds from an
environment of the user. The hearing aid system may also include at
least one processor programmed to: receive a first plurality of
images captured by the camera; identify a representation of an
individual in the first plurality of images; receive from the at
least one microphone a first audio signal representative of a
voice; determine, based on analysis of the first plurality of
images, that the first audio signal representative of a voice is
associated with the individual; selectively amplify the first audio
signal over other audio signals received from the at least one
microphone representative of sounds from sources other than the
individual; receive a second plurality of images captured by the
camera; determine, based on analysis of the second plurality of
images, that the individual is not represented in the second
plurality of images; receive from the at least one microphone a
second audio signal representative of a voice; determine, based on
analysis of the first audio signal and the second audio signal,
that the second audio signal is associated with the individual;
selectively amplify the second audio signal over other received
audio signals representative of sounds from sources other than the
individual; and cause transmission of at least one of the
selectively amplified first audio signal or the selectively
amplified second audio signal to a hearing interface device
configured to provide sound to an ear of the user.
[0019] In an embodiment, a hearing aid system for selectively
amplifying audio signals may comprise a wearable camera configured
to capture a plurality of images from an environment of a user and
at least one microphone configured to capture sounds from an
environment of the user. The hearing aid system may also include at
least one processor programmed to: receive the plurality of images
captured by the camera; identify a representation of one or more
individuals in the plurality of images; receive from the at least
one microphone an audio signal associated with a voice; determine,
based on analysis of the plurality of images, that the audio signal
is not associated with a voice of any of the one or more
individuals; determine, based on analysis of the audio signal, that
the audio signal is associated with at least one indicator that the
audio signal is related to a public announcement; cause selective
amplification of the audio signal based on the determination that
the audio signal is associated with at least one indicator that the
audio signal relates to a public announcement; and cause
transmission of the selectively amplified audio signal to a hearing
interface device configured to provide sound to an ear of the
user.
[0020] In an embodiment, a hearing aid system is provided. The
system may include a wearable camera configured to capture a
plurality of images from an environment of a user; at least one
microphone configured to capture sounds from an environment of the
user; and at least one processor. The processor may be programmed
to receive the plurality of images captured by the camera; identify
a representation of at least one individual in at least one of the
plurality of images, and determine whether the at least one
individual is a recognized individual. Further, if the at least one
individual is determined to be a recognized individual, cause an
image of the at least one individual to be shown on a display and
selectively condition at least one audio signal that is received
from the at least one microphone and determined to be associated
with the recognized individual; and cause transmission of the at
least one conditioned audio signal to a hearing interface device
configured to provide sound to an ear of the user.
[0021] In an embodiment, a hearing aid system is provided. The
system may include a wearable camera configured to capture a
plurality of images from an environment of a user; at least one
microphone configured to capture sounds from an environment of the
user; and at least one processor. The processor may be programmed
to receive an audio signal from the at least one microphone and
determine whether the received audio signal is associated with a
recognized individual. Further, if the at least one individual is
determined to be a recognized individual, cause an image of the at
least one individual to be shown on a display and selectively
condition the audio signal, and cause transmission of the
conditioned audio signal to a hearing interface device configured
to provide sound to an ear of the user.
[0022] In an embodiment, a hearing aid system is provided. The
system may include a wearable camera configured to capture a
plurality of images from an environment of a user; at least one
microphone configured to capture sounds from an environment of the
user; and at least one processor. The processor may be programmed
to receive audio signals from the at least one microphone; detect,
based on analysis of the audio signals, a first audio signal
associated with a first time period, wherein the first audio signal
is representative of the voice of a single individual; detect,
based on analysis of the audio signals, a second audio signal
associated with a second time period, wherein the second time
period is different from the first time period, and wherein the
second audio signal is representative of overlapping voices of two
or more individuals; selectively condition the first audio signal
and the second audio signal, wherein the selective conditioning of
the first audio signal is different in at least one respect
relative the selective conditioning of the second audio signal; and
cause transmission of the conditioned first audio signal to a
hearing interface device configured to provide sound to an ear of
the user.
[0023] In an embodiment, a hearing aid system is disclosed. The
system includes a wearable camera configured to capture a plurality
of images from an environment of a user, at least one microphone
configured to capture sounds from the environment of the user, and
at least one processor programmed to: receive the plurality of
images captured by the wearable camera; receive audio signals
representative of sounds captured by the at least one microphone;
identify a first audio signal, from among the received audio
signals, representative of a voice of a first individual;
transcribe and store, in a memory, text corresponding to speech
associated with the voice of the first individual; determine
whether the first individual is a recognized individual; and if the
first individual is a recognized individual, associate an
identifier of the first recognized individual with the stored text
corresponding to speech associated with the voice of the first
individual.
[0024] In an embodiment, a computer-implemented method for
individual identification of a hearing aid system is disclosed. The
method includes: receiving a plurality of images from a wearable
camera; receiving audio signals representative of sounds from at
least one microphone; identifying a first audio signal, from among
the received audio signals, representative of a voice of a first
individual; transcribing and storing text corresponding to speech
associated with the voice of the first individual; determining
whether the first individual is a recognized individual; and if the
first individual is a recognized individual, associating an
identifier of the first recognized individual with the stored text
corresponding to speech associated with the voice of the first
individual.
[0025] In an embodiment, a non-transitory computer readable storage
media is disclosed. The non-transitory computer readable storage
media stores program instructions which are executed by at least
one processor to perform: receiving a plurality of images from a
wearable camera; receiving audio signals representative of sounds
from at least one microphone; identifying a first individual
represented in at least one of the plurality of images; identifying
a first audio signal, from among the received audio signals,
representative of a voice of the a first individual; transcribing
and storing text corresponding to speech associated with the voice
of the first individual; determining whether the first individual
is a recognized individual; and if the first individual is a
recognized individual, associating an identifier of the first
recognized individual with the stored text corresponding to the
speech associated with the voice of the first individual.
[0026] In an embodiment, a hearing aid system for selectively
conditioning audio signals associated with a recognized object is
provided. The system may include at least one processor programmed
to receive audio signals acquired by a wearable microphone, wherein
the audio signals are representative of sounds emanating from
objects in an environment of a user. The at least one processor may
analyze the received audio signals to obtain an isolated audio
stream associated with a sound-emanating object in the environment
of the user. Further, the at least one processor may determine an
audioprint from the isolated audio stream and may use the
audioprint to retrieve from a database information relating to the
particular sound-emanating object. Based on the retrieved
information, the at least one processor may cause selective
conditioning of at least one audio signal received by the wearable
microphone from a region associated with the at least one
sound-emanating object, and may cause transmission of the at least
one conditioned audio signal to a hearing interface device
configured to provide sounds to an ear of the user.
[0027] In an embodiment, a method is provided for selectively
conditioning audio signals associated with a recognized object. The
method may comprise receiving audio signals acquired by a wearable
microphone, wherein the audio signals are representative of sounds
emanating from objects in an environment of a user; analyzing the
received audio signals to isolate an audio stream determined to be
associated with a particular sound-emanating object in the
environment of the user; determining an audioprint of the isolated
audio stream; using the determined audioprint to retrieve from a
database information relating to the particular sound-emanating
object; based on the retrieved information, causing selective
conditioning of at least one audio signal received by the wearable
microphone from a region associated with the at least one
sound-emanating object; and causing transmission of the at least
one conditioned audio signal to a hearing interface device
configured to provide sounds to an ear of the user.
[0028] In an embodiment, a hearing aid system for selectively
conditioning audio signals associated with a recognized object is
provided. The system may include at least one processor programmed
to receive a plurality of images from an environment of a user
captured by a wearable camera. The at least one processor may
process the plurality of images to detect a sound-emanating object
in at least one of the plurality of images, and identify the
sound-emanating object using the at least one of the plurality of
images. The at least one processor may further use the determined
identity of the sound-emanating object to retrieve from a database
information relating to the sound-emanating object. The at least
one processor may also receive at least one audio signal acquired
by a wearable microphone, wherein the at least one audio signal is
representative of sounds including a sound emanating from the
sound-emanating object, and separate the at least one audio signal
using the retrieved information to isolate the sound emanating from
the sound-emanating object, cause selective conditioning of the
sound to obtain at least one conditioned audio signal, and may
cause transmission of the at least one conditioned audio signal to
a hearing interface device configured to provide sounds to an ear
of the user.
[0029] In an embodiment, a hearing aid system for selective
modification of background noises is provided. The system may
include and at least one processor programmed to receive a
plurality of images from an environment of a user captured by a
wearable camera during a time period, and receive at least one
audio signal representative of sounds acquired by a wearable
microphone during the time period. Further, the at least one
processor may determine that at least one of the sounds was
generated by a sound-emanating object in the environment of the
user, but outside of a field of view of the wearable camera, and
retrieve from a database information associated with the at least
one sound. Based on the retrieved information, the at least one
processor may cause selective conditioning of audio signals
acquired by the wearable microphone during the time period and
causes transmission of the conditioned audio signals to a hearing
interface device configured to provide sounds to an ear of the
user.
[0030] In an embodiment, a method is provided for selective
modification of different types of background noises. The method
may comprise receiving a plurality of images from an environment of
a user captured by a wearable camera during a time period;
receiving audio signals representative of sounds from the
environment of the user acquired by a wearable microphone during
the time period; determining that at least one of the sounds was
generated in response to sounds from a sound-emanating object in
the environment of the user, but outside of a field of view of the
wearable camera; retrieving from a database information associated
with the at least one of the sounds based on the retrieved
information, causing selective conditioning of audio signals
acquired by the wearable microphone during the time period; and
causing transmission of the conditioned audio signals to a hearing
interface device configured to provide sounds to an ear of the
user.
[0031] In an embodiment, a system for identifying sound-emanating
objects in an environment of a user is disclosed. The system may
comprise at least one memory device configured to store a database
of reference visual characteristics and reference voiceprints
corresponding to a plurality of objects; and at least one
processor. The processor may be programmed to receive a plurality
of images captured by a wearable camera, wherein at least one of
the plurality of images depicts at least one sound-emanating object
in an environment of a user; analyze the received at least one of
the plurality of images to determine one or more visual
characteristics associated with the at least one sound-emanating
object; identify within the database in view of the one or more
visual characteristics, the at least one sound-emanating object and
determine a degree of certainty of identification; receive audio
signals acquired by a wearable microphone, wherein the audio
signals are representative of one or more sounds emanating from the
at least one sound-emanating object; analyze the received audio
signals to determine a voiceprint of the at least one
sound-emanating object; when the degree of certainty of
identification falls below a predetermined level, further identify
the at least one sound-emanating object based on the determined
voiceprint; and initiate at least one action based on an identity
of the at least one sound-emanating object.
[0032] In an embodiment, a method for identifying sound-emanating
objects in an environment of a user is disclosed. The method may
comprise accessing a database of reference visual signatures and
reference voice signatures corresponding to a plurality of objects;
receiving a plurality of images captured by a wearable camera,
wherein at least one of the plurality of images depicts at least
one sound-emanating object in an environment of a user; analyzing
the received at least one of the plurality of images to determine
one or more visual characteristics associated with the at least one
sound-emanating object; identifying, based on review of the
database in view of the one or more visual characteristics, the at
least one sound-emanating object and determine a degree of
certainty of identification; receiving audio signals acquired by a
wearable microphone, wherein the audio signals are representative
of one or more sounds emanating from the at least one
sound-emanating object; analyzing the received audio signals to
determine voiceprint of the at least one sound-emanating object;
when the degree of certainty of identification falls below a
predetermined level, further identifying the at least one
sound-emanating object based on the determined voiceprint; and
initiating at least one action based on an identity of the at least
one sound-emanating object.
[0033] In an embodiment, a software product may be stored on a
non-transitory computer readable medium and may comprise computer
implementable instructions for a method for identifying
sound-emanating objects. The method may comprise accessing a
database of reference visual signatures and reference voice
signatures corresponding to a plurality of objects; receiving a
plurality of images captured by a wearable camera, wherein at least
one of the plurality of images depicts at least one sound-emanating
object in an environment of a user; analyzing the received at least
one of the plurality of images to determine one or more visual
characteristics associated with the at least one sound-emanating
object; identifying, based on review of the database in view of the
one or more visual characteristics, the at least one
sound-emanating object and determine a degree of certainty of
identification; receiving audio signals acquired by a wearable
microphone, wherein the audio signals are representative of one or
more sounds emanating from the at least one sound-emanating object;
analyzing the received audio signals to determine voiceprint of the
at least one sound-emanating object; when the degree of certainty
of identification falls below a predetermined level, further
identifying the at least one sound-emanating object based on the
determined voiceprint; and initiating at least one action based on
an identity of the at least one sound-emanating object.
[0034] In an embodiment, a hearing aid system may selectively
condition audio signals. The hearing aid system may include at
least one processor programmed to receive a plurality of images
captured by a wearable camera, wherein the plurality of images
depict objects in an environment of a user; receive audio signals
acquired by a wearable microphone, wherein the audio signals are
representative of sounds emanating from the objects; analyze the
plurality of images to identify at least one sound-emanating object
in the environment of the user; retrieve from a database
information about the at least one identified sound-emanating
object; based on the retrieved information, cause selective
conditioning of at least one audio signal received by the wearable
microphone from a region associated with the at least one
sound-emanating object; cause transmission of the at least one
conditioned audio signal to a hearing interface device configured
to provide sounds to an ear of the user.
[0035] In an embodiment, a method for modifying sounds emanating
from objects in an environment of a user is disclosed. The method
may comprise receiving a plurality of images captured by a wearable
camera, wherein the plurality of images depict objects in an
environment of a user; receiving audio signals acquired by a
wearable microphone, wherein the audio signals are representative
of sounds emanating from the objects; analyzing the plurality of
images to identify at least one sound-emanating object in the
environment of the user; retrieving from a database information
about the at least one sound-emanating object; based on the
retrieved information, causing selective conditioning of at least
one audio signal acquired by the wearable microphone from a region
associated with the at least one sound-emanating object; causing
transmission of the at least one conditioned audio signal to a
hearing interface device configured to provide sounds to an ear of
the user.
[0036] In an embodiment, a software product may be stored on a
non-transitory computer readable medium and may comprise computer
implementable instructions for a method for identifying
sound-emanating objects. The method may comprise accessing a
database of reference visual signatures and reference voice
signatures corresponding to a plurality of objects; receiving a
plurality of images captured by a wearable camera, wherein at least
one of the plurality of images depicts at least one sound-emanating
object in an environment of a user; analyzing the received at least
one of the plurality of images to determine one or more visual
characteristics associated with the at least one sound-emanating
object; identifying, based on review of the database in view of the
one or more visual characteristics, the at least one
sound-emanating object and determine a degree of certainty of
identification; receiving audio signals acquired by a wearable
microphone, wherein the audio signals are representative of one or
more sounds emanating from the at least one sound-emanating object;
analyzing the received audio signals to determine voiceprint of the
at least one sound-emanating object; when the degree of certainty
of identification falls below a predetermined level, further
identifying the at least one sound-emanating object based on the
determined voiceprint; and initiating at least one action based on
an identity of the at least one sound-emanating object.
[0037] In an embodiment, a hearing interface device is disclosed.
The hearing interface device may comprise a receiver configured to
receive at least one audio signal, wherein the at least one audio
signal was acquired by a wearable microphone and was selectively
conditioned by at least one processor configured to receive a
plurality of images captured by a wearable camera, identify at
least one sound-emanating object in the plurality of images, and
cause the conditioning based on retrieved information about the at
least one sound-emanating object. The hearing aid device may
further comprise an electroacoustic transducer configured to
provide sounds from the at least one audio signal to an ear of the
user.
[0038] Consistent with other disclosed embodiments, non-transitory
computer-readable storage media may store program instructions,
which are executed by at least one processor and perform any of the
methods described herein.
[0039] The foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various disclosed
embodiments. In the drawings:
[0041] FIG. 1A is a schematic illustration of an example of a user
wearing a wearable apparatus according to a disclosed
embodiment.
[0042] FIG. 1B is a schematic illustration of an example of the
user wearing a wearable apparatus according to a disclosed
embodiment.
[0043] FIG. 1C is a schematic illustration of an example of the
user wearing a wearable apparatus according to a disclosed
embodiment.
[0044] FIG. 1D is a schematic illustration of an example of the
user wearing a wearable apparatus according to a disclosed
embodiment.
[0045] FIG. 2 is a schematic illustration of an example system
consistent with the disclosed embodiments.
[0046] FIG. 3A is a schematic illustration of an example of the
wearable apparatus shown in FIG. 1A.
[0047] FIG. 3B is an exploded view of the example of the wearable
apparatus shown in FIG. 3A.
[0048] FIG. 4A-4K are schematic illustrations of an example of the
wearable apparatus shown in FIG. 1B from various viewpoints.
[0049] FIG. 5A is a block diagram illustrating an example of the
components of a wearable apparatus according to a first
embodiment.
[0050] FIG. 5B is a block diagram illustrating an example of the
components of a wearable apparatus according to a second
embodiment.
[0051] FIG. 5C is a block diagram illustrating an example of the
components of a wearable apparatus according to a third
embodiment.
[0052] FIG. 6 illustrates an exemplary embodiment of a memory
containing software modules consistent with the present
disclosure.
[0053] FIG. 7 is a schematic illustration of an embodiment of a
wearable apparatus including an orientable image capture unit.
[0054] FIG. 8 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0055] FIG. 9 is a schematic illustration of a user wearing a
wearable apparatus consistent with an embodiment of the present
disclosure.
[0056] FIG. 10 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0057] FIG. 11 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0058] FIG. 12 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0059] FIG. 13 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0060] FIG. 14 is a schematic illustration of an embodiment of a
wearable apparatus securable to an article of clothing consistent
with the present disclosure.
[0061] FIG. 15 is a schematic illustration of an embodiment of a
wearable apparatus power unit including a power source.
[0062] FIG. 16 is a schematic illustration of an exemplary
embodiment of a wearable apparatus including protective
circuitry.
[0063] FIG. 17A is a schematic illustration of an example of a user
wearing an apparatus for a camera-based hearing aid device
according to a disclosed embodiment.
[0064] FIG. 17B is a schematic illustration of an embodiment of an
apparatus securable to an article of clothing consistent with the
present disclosure.
[0065] FIG. 18 is a schematic illustration showing an exemplary
environment for use of a camera-based hearing aid consistent with
the present disclosure.
[0066] FIG. 19 is a flowchart showing an exemplary process for
selectively amplifying sounds emanating from a detected look
direction of a user consistent with disclosed embodiments.
[0067] FIG. 20A is a schematic illustration showing an exemplary
environment for use of a hearing aid with voice and/or image
recognition consistent with the present disclosure.
[0068] FIG. 20B illustrates an exemplary embodiment of an apparatus
comprising facial and voice recognition components consistent with
the present disclosure.
[0069] FIG. 21 is a flowchart showing an exemplary process for
selectively amplifying audio signals associated with a voice of a
recognized individual consistent with disclosed embodiments.
[0070] FIG. 22 is a flowchart showing an exemplary process for
selectively transmitting audio signals associated with a voice of a
recognized user consistent with disclosed embodiments.
[0071] FIG. 23A is a schematic illustration showing an exemplary
individual that may be identified in the environment of a user
consistent with the present disclosure.
[0072] FIG. 23B is a schematic illustration showing an exemplary
individual that may be identified in the environment of a user
consistent with the present disclosure.
[0073] FIG. 23C illustrates an exemplary lip-tracking system
consistent with the disclosed embodiments.
[0074] FIG. 24 is a schematic illustration showing an exemplary
environment for use of a lip-tracking hearing aid consistent with
the present disclosure.
[0075] FIG. 25 is a flowchart showing an exemplary process for
selectively amplifying audio signals based on tracked lip movements
consistent with disclosed embodiments.
[0076] FIG. 26 is a schematic illustration of an exemplary hearing
aid system consistent with the present disclosure.
[0077] FIG. 27 is a schematic illustration of an exemplary image
captured by an imaging capture device consistent with the present
disclosure.
[0078] FIG. 28 is a flowchart of an exemplary process for
selectively amplifying an audio signal.
[0079] FIG. 29 is a schematic illustration of an exemplary hearing
aid system consistent with the present disclosure.
[0080] FIGS. 30A and 30B are schematic illustrations of exemplary
images captured by an imaging capture device consistent with the
present disclosure.
[0081] FIG. 31A is a flowchart of an exemplary process for
selectively amplifying audio signals.
[0082] FIG. 31B is a flowchart of an exemplary process for
selectively amplifying audio signals.
[0083] FIG. 31C is a flowchart of an exemplary process for
selectively amplifying audio signals.
[0084] FIG. 32 is a schematic illustration of an example system
including a wearable apparatus according to a disclosed
embodiment.
[0085] FIG. 33 is an example illustration of a user with a wearable
device communicating with other people according to a disclosed
embodiment.
[0086] FIGS. 34A and 34B are example flowcharts describing a
process of isolating one or more voices of different speakers from
an audio signal according to disclosed embodiments.
[0087] FIG. 35A is an example flowchart describing a process of
separating a voice of a speaker from an audio signal according to
disclosed embodiments.
[0088] FIG. 35B is an example flowchart describing a process of
transmitting to a hearing device a conditioned audio signal
according to disclosed embodiments.
[0089] FIG. 36A is an example flowchart describing a process of
separating a voice of a speaker from an audio signal according to
disclosed embodiments.
[0090] FIG. 36B is a block diagram of modules of a wearable
apparatus consistent with the disclosed embodiments.
[0091] FIGS. 37A-37C are example flowcharts describing a process of
transmitting to a hearing device a conditioned audio signal
according to disclosed embodiments.
[0092] FIG. 38A is a block diagram illustrating a hearing aid
system according to an example embodiment.
[0093] FIG. 38B is a schematic illustration showing an exemplary
environment for use of a hearing aid with instruction deduction
consistent with the present disclosure.
[0094] FIG. 38C is a schematic illustration of an exemplary hearing
aid system consistent with the present disclosure.
[0095] FIGS. 39A and 39B are flowcharts illustrating processes for
deducing instructions for a hearing aid system according to a first
embodiment.
[0096] FIGS. 40A and 40B are flowcharts illustrating processes for
deducing instructions for a hearing aid system according to a
second embodiment.
[0097] FIG. 41A is a block chart illustrating an exemplary
embodiment of a memory device containing software modules
consistent with the present disclosure.
[0098] FIG. 41B is a schematic illustration showing an exemplary
environment of user of a hearing aid system that selectively
conditions audio signals consistent with the present
disclosure.
[0099] FIGS. 42A-42F are schematic illustrations of audio signals
acquired and processed by the hearing aid system illustrated in
FIG. 41B consistent with the present disclosure.
[0100] FIG. 43A is a flowchart showing an exemplary process for
selectively conditioning audio signals associated with a recognized
object, consistent with disclosed embodiments.
[0101] FIG. 43B is a flowchart showing another exemplary process
for selectively conditioning audio signals associated with a
recognized object, consistent with disclosed embodiments.
[0102] FIG. 44A is a schematic illustration showing an exemplary
environment of a user that includes sound-emanating objects
responsible for background noises consistent with the present
disclosure.
[0103] FIG. 44B is a schematic illustration of the audio signals
acquired by a wearable microphone in the scenario illustrated in
FIG. 44A, consistent with the present disclosure.
[0104] FIG. 44C is a schematic illustration of the conditioned
audio signals transmitted to a hearing interface device in the
scenario illustrated in FIG. 44A, consistent with the present
disclosure.
[0105] FIG. 45 is a block diagram illustrating an example of the
components of a hearing interface device consistent with the
present disclosure.
[0106] FIG. 46A is a flowchart showing an exemplary process for
selective modification of background noises based on determined
importance levels, consistent with disclosed embodiments.
[0107] FIG. 46B is a flowchart showing an exemplary process for
selective modification of background noises, consistent with
disclosed embodiments.
[0108] FIG. 47A is a block diagram illustrating an exemplary
hearing aid system consistent with the present disclosure.
[0109] FIG. 47B is a schematic illustration showing an exemplary
environment for using voice and visual signatures to identify
objects consistent with the present disclosure.
[0110] FIG. 48 is an illustration showing an exemplary device
displaying the name of a sound emanating object with the present
disclosure.
[0111] FIG. 49 is a flowchart showing an exemplary process for
using voice and visual signatures to identify objects consistent
with disclosed embodiments.
[0112] FIG. 50A is a schematic illustration showing examples of
sound emitting objects that may be identified in the environment of
a user consistent with the present disclosure.
[0113] FIG. 50B is an illustration of an example database storing
information associated with sound emanating objects consistent with
the present disclosure.
[0114] FIGS. 51A and 51B are schematic illustrations showing
example environments for selectively conditioning audio signals
consistent with the present disclosure.
[0115] FIG. 52 is a flowchart showing an exemplary process for
modifying sounds emanating from objects in an environment of a user
consistent with the disclosed embodiments.
DETAILED DESCRIPTION
[0116] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar parts. While several illustrative
embodiments are described herein, modifications, adaptations and
other implementations are possible. For example, substitutions,
additions or modifications may be made to the components
illustrated in the drawings, and the illustrative methods described
herein may be modified by substituting, reordering, removing, or
adding steps to the disclosed methods. Accordingly, the following
detailed description is not limited to the disclosed embodiments
and examples. Instead, the proper scope is defined by the appended
claims.
[0117] FIG. 1A illustrates a user 100 wearing an apparatus 110 that
is physically connected (or integral) to glasses 130, consistent
with the disclosed embodiments. Glasses 130 may be prescription
glasses, magnifying glasses, non-prescription glasses, safety
glasses, sunglasses, etc. Additionally, in some embodiments,
glasses 130 may include parts of a frame and earpieces, nosepieces,
etc., and one or no lenses. Thus, in some embodiments, glasses 130
may function primarily to support apparatus 110, and/or an
augmented reality display device or other optical display device.
In some embodiments, apparatus 110 may include an image sensor (not
shown in FIG. 1A) for capturing real-time image data of the
field-of-view of user 100. The term "image data" includes any form
of data retrieved from optical signals in the near-infrared,
infrared, visible, and ultraviolet spectrums. The image data may
include video clips and/or photographs.
[0118] In some embodiments, apparatus 110 may communicate
wirelessly or via a wire with a computing device 120. In some
embodiments, computing device 120 may include, for example, a
smartphone, or a tablet, or a dedicated processing unit, which may
be portable (e.g., can be carried in a pocket of user 100).
Although shown in FIG. 1A as an external device, in some
embodiments, computing device 120 may be provided as part of
wearable apparatus 110 or glasses 130, whether integral thereto or
mounted thereon. In some embodiments, computing device 120 may be
included in an augmented reality display device or optical head
mounted display provided integrally or mounted to glasses 130. In
other embodiments, computing device 120 may be provided as part of
another wearable or portable apparatus of user 100 including a
wrist-strap, a multifunctional watch, a button, a clip-on, etc. And
in other embodiments, computing device 120 may be provided as part
of another system, such as an on-board automobile computing or
navigation system. A person skilled in the art can appreciate that
different types of computing devices and arrangements of devices
may implement the functionality of the disclosed embodiments.
Accordingly, in other implementations, computing device 120 may
include a Personal Computer (PC), laptop, an Internet server,
etc.
[0119] FIG. 1B illustrates user 100 wearing apparatus 110 that is
physically connected to a necklace 140, consistent with a disclosed
embodiment. Such a configuration of apparatus 110 may be suitable
for users that do not wear glasses some or all of the time. In this
embodiment, user 100 can easily wear apparatus 110, and take it
off.
[0120] FIG. 1C illustrates user 100 wearing apparatus 110 that is
physically connected to a belt 150, consistent with a disclosed
embodiment. Such a configuration of apparatus 110 may be designed
as a belt buckle. Alternatively, apparatus 110 may include a clip
for attaching to various clothing articles, such as belt 150, or a
vest, a pocket, a collar, a cap or hat or other portion of a
clothing article.
[0121] FIG. 1D illustrates user 100 wearing apparatus 110 that is
physically connected to a wrist strap 160, consistent with a
disclosed embodiment. Although the aiming direction of apparatus
110, according to this embodiment, may not match the field-of-view
of user 100, apparatus 110 may include the ability to identify a
hand-related trigger based on the tracked eye movement of a user
100 indicating that user 100 is looking in the direction of the
wrist strap 160. Wrist strap 160 may also include an accelerometer,
a gyroscope, or other sensor for determining movement or
orientation of a user's 100 hand for identifying a hand-related
trigger.
[0122] FIG. 2 is a schematic illustration of an exemplary system
200 including a wearable apparatus 110, worn by user 100, and an
optional computing device 120 and/or a server 250 capable of
communicating with apparatus 110 via a network 240, consistent with
disclosed embodiments. In some embodiments, apparatus 110 may
capture and analyze image data, identify a hand-related trigger
present in the image data, and perform an action and/or provide
feedback to a user 100, based at least in part on the
identification of the hand-related trigger. In some embodiments,
optional computing device 120 and/or server 250 may provide
additional functionality to enhance interactions of user 100 with
his or her environment, as described in greater detail below.
[0123] According to the disclosed embodiments, apparatus 110 may
include an image sensor system 220 for capturing real-time image
data of the field-of-view of user 100. In some embodiments,
apparatus 110 may also include a processing unit 210 for
controlling and performing the disclosed functionality of apparatus
110, such as to control the capture of image data, analyze the
image data, and perform an action and/or output a feedback based on
a hand-related trigger identified in the image data. According to
the disclosed embodiments, a hand-related trigger may include a
gesture performed by user 100 involving a portion of a hand of user
100. Further, consistent with some embodiments, a hand-related
trigger may include a wrist-related trigger. Additionally, in some
embodiments, apparatus 110 may include a feedback outputting unit
230 for producing an output of information to user 100.
[0124] As discussed above, apparatus 110 may include an image
sensor 220 for capturing image data. The term "image sensor" refers
to a device capable of detecting and converting optical signals in
the near-infrared, infrared, visible, and ultraviolet spectrums
into electrical signals. The electrical signals may be used to form
an image or a video stream (i.e. image data) based on the detected
signal. The term "image data" includes any form of data retrieved
from optical signals in the near-infrared, infrared, visible, and
ultraviolet spectrums. Examples of image sensors may include
semiconductor charge-coupled devices (CCD), active pixel sensors in
complementary metal-oxide-semiconductor (CMOS), or N-type
metal-oxide-semiconductor (NMOS, Live MOS). In some cases, image
sensor 220 may be part of a camera included in apparatus 110.
[0125] Apparatus 110 may also include a processor 210 for
controlling image sensor 220 to capture image data and for
analyzing the image data according to the disclosed embodiments. As
discussed in further detail below with respect to FIG. 5A,
processor 210 may include a "processing device" for performing
logic operations on one or more inputs of image data and other data
according to stored or accessible software instructions providing
desired functionality. In some embodiments, processor 210 may also
control feedback outputting unit 230 to provide feedback to user
100 including information based on the analyzed image data and the
stored software instructions. As the term is used herein, a
"processing device" may access memory where executable instructions
are stored or, in some embodiments, a "processing device" itself
may include executable instructions (e.g., stored in memory
included in the processing device).
[0126] In some embodiments, the information or feedback information
provided to user 100 may include time information. The time
information may include any information related to a current time
of day and, as described further below, may be presented in any
sensory perceptive manner. In some embodiments, time information
may include a current time of day in a preconfigured format (e.g.,
2:30 pm or 14:30). Time information may include the time in the
user's current time zone (e.g., based on a determined location of
user 100), as well as an indication of the time zone and/or a time
of day in another desired location. In some embodiments, time
information may include a number of hours or minutes relative to
one or more predetermined times of day. For example, in some
embodiments, time information may include an indication that three
hours and fifteen minutes remain until a particular hour (e.g.,
until 6:00 pm), or some other predetermined time. Time information
may also include a duration of time passed since the beginning of a
particular activity, such as the start of a meeting or the start of
a jog, or any other activity. In some embodiments, the activity may
be determined based on analyzed image data. In other embodiments,
time information may also include additional information related to
a current time and one or more other routine, periodic, or
scheduled events. For example, time information may include an
indication of the number of minutes remaining until the next
scheduled event, as may be determined from a calendar function or
other information retrieved from computing device 120 or server
250, as discussed in further detail below.
[0127] Feedback outputting unit 230 may include one or more
feedback systems for providing the output of information to user
100. In the disclosed embodiments, the audible or visual feedback
may be provided via any type of connected audible or visual system
or both. Feedback of information according to the disclosed
embodiments may include audible feedback to user 100 (e.g., using a
Bluetooth.TM. or other wired or wirelessly connected speaker, or a
bone conduction headphone). Feedback outputting unit 230 of some
embodiments may additionally or alternatively produce a visible
output of information to user 100, for example, as part of an
augmented reality display projected onto a lens of glasses 130 or
provided via a separate heads up display in communication with
apparatus 110, such as a display 260 provided as part of computing
device 120, which may include an onboard automobile heads up
display, an augmented reality device, a virtual reality device, a
smartphone, PC, table, etc.
[0128] The term "computing device" refers to a device including a
processing unit and having computing capabilities. Some examples of
computing device 120 include a PC, laptop, tablet, or other
computing systems such as an on-board computing system of an
automobile, for example, each configured to communicate directly
with apparatus 110 or server 250 over network 240. Another example
of computing device 120 includes a smartphone having a display 260.
In some embodiments, computing device 120 may be a computing system
configured particularly for apparatus 110, and may be provided
integral to apparatus 110 or tethered thereto. Apparatus 110 can
also connect to computing device 120 over network 240 via any known
wireless standard (e.g., Wi-Fi, Bluetooth.RTM., etc.), as well as
near-filed capacitive coupling, and other short range wireless
techniques, or via a wired connection. In an embodiment in which
computing device 120 is a smartphone, computing device 120 may have
a dedicated application installed therein. For example, user 100
may view on display 260 data (e.g., images, video clips, extracted
information, feedback information, etc.) that originate from or are
triggered by apparatus 110. In addition, user 100 may select part
of the data for storage in server 250.
[0129] Network 240 may be a shared, public, or private network, may
encompass a wide area or local area, and may be implemented through
any suitable combination of wired and/or wireless communication
networks. Network 240 may further comprise an intranet or the
Internet. In some embodiments, network 240 may include short range
or near-field wireless communication systems for enabling
communication between apparatus 110 and computing device 120
provided in close proximity to each other, such as on or near a
user's person, for example. Apparatus 110 may establish a
connection to network 240 autonomously, for example, using a
wireless module (e.g., Wi-Fi, cellular). In some embodiments,
apparatus 110 may use the wireless module when being connected to
an external power source, to prolong battery life. Further,
communication between apparatus 110 and server 250 may be
accomplished through any suitable communication channels, such as,
for example, a telephone network, an extranet, an intranet, the
Internet, satellite communications, off-line communications,
wireless communications, transponder communications, a local area
network (LAN), a wide area network (WAN), and a virtual private
network (VPN).
[0130] As shown in FIG. 2, apparatus 110 may transfer or receive
data to/from server 250 via network 240. In the disclosed
embodiments, the data being received from server 250 and/or
computing device 120 may include numerous different types of
information based on the analyzed image data, including information
related to a commercial product, or a person's identity, an
identified landmark, and any other information capable of being
stored in or accessed by server 250. In some embodiments, data may
be received and transferred via computing device 120. Server 250
and/or computing device 120 may retrieve information from different
data sources (e.g., a user specific database or a user's social
network account or other account, the Internet, and other managed
or accessible databases) and provide information to apparatus 110
related to the analyzed image data and a recognized trigger
according to the disclosed embodiments. In some embodiments,
calendar-related information retrieved from the different data
sources may be analyzed to provide certain time information or a
time-based context for providing certain information based on the
analyzed image data.
[0131] An example of wearable apparatus 110 incorporated with
glasses 130 according to some embodiments (as discussed in
connection with FIG. 1A) is shown in greater detail in FIG. 3A. In
some embodiments, apparatus 110 may be associated with a structure
(not shown in FIG. 3A) that enables easy detaching and reattaching
of apparatus 110 to glasses 130. In some embodiments, when
apparatus 110 attaches to glasses 130, image sensor 220 acquires a
set aiming direction without the need for directional calibration.
The set aiming direction of image sensor 220 may substantially
coincide with the field-of-view of user 100. For example, a camera
associated with image sensor 220 may be installed within apparatus
110 in a predetermined angle in a position facing slightly
downwards (e.g., 5-15 degrees from the horizon). Accordingly, the
set aiming direction of image sensor 220 may substantially match
the field-of-view of user 100.
[0132] FIG. 3B is an exploded view of the components of the
embodiment discussed regarding FIG. 3A. Attaching apparatus 110 to
glasses 130 may take place in the following way. Initially, a
support 310 may be mounted on glasses 130 using a screw 320, in the
side of support 310. Then, apparatus 110 may be clipped on support
310 such that it is aligned with the field-of-view of user 100. The
term "support" includes any device or structure that enables
detaching and reattaching of a device including a camera to a pair
of glasses or to another object (e.g., a helmet). Support 310 may
be made from plastic (e.g., polycarbonate), metal (e.g., aluminum),
or a combination of plastic and metal (e.g., carbon fiber
graphite). Support 310 may be mounted on any kind of glasses (e.g.,
eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using
screws, bolts, snaps, or any fastening means used in the art.
[0133] In some embodiments, support 310 may include a quick release
mechanism for disengaging and reengaging apparatus 110. For
example, support 310 and apparatus 110 may include magnetic
elements. As an alternative example, support 310 may include a male
latch member and apparatus 110 may include a female receptacle. In
other embodiments, support 310 can be an integral part of a pair of
glasses, or sold separately and installed by an optometrist. For
example, support 310 may be configured for mounting on the arms of
glasses 130 near the frame front, but before the hinge.
Alternatively, support 310 may be configured for mounting on the
bridge of glasses 130.
[0134] In some embodiments, apparatus 110 may be provided as part
of a glasses frame 130, with or without lenses. Additionally, in
some embodiments, apparatus 110 may be configured to provide an
augmented reality display projected onto a lens of glasses 130 (if
provided), or alternatively, may include a display for projecting
time information, for example, according to the disclosed
embodiments. Apparatus 110 may include the additional display or
alternatively, may be in communication with a separately provided
display system that may or may not be attached to glasses 130.
[0135] In some embodiments, apparatus 110 may be implemented in a
form other than wearable glasses, as described above with respect
to FIGS. 1B-1D, for example. FIG. 4A is a schematic illustration of
an example of an additional embodiment of apparatus 110 from a
front viewpoint of apparatus 110. Apparatus 110 includes an image
sensor 220, a clip (not shown), a function button (not shown) and a
hanging ring 410 for attaching apparatus 110 to, for example,
necklace 140, as shown in FIG. 1B. When apparatus 110 hangs on
necklace 140, the aiming direction of image sensor 220 may not
fully coincide with the field-of-view of user 100, but the aiming
direction would still correlate with the field-of-view of user
100.
[0136] FIG. 4B is a schematic illustration of the example of a
second embodiment of apparatus 110, from a side orientation of
apparatus 110. In addition to hanging ring 410, as shown in FIG.
4B, apparatus 110 may further include a clip 420. User 100 can use
clip 420 to attach apparatus 110 to a shirt or belt 150, as
illustrated in FIG. 1C. Clip 420 may provide an easy mechanism for
disengaging and reengaging apparatus 110 from different articles of
clothing. In other embodiments, apparatus 110 may include a female
receptacle for connecting with a male latch of a car mount or
universal stand.
[0137] In some embodiments, apparatus 110 includes a function
button 430 for enabling user 100 to provide input to apparatus 110.
Function button 430 may accept different types of tactile input
(e.g., a tap, a click, a double-click, a long press, a
right-to-left slide, a left-to-right slide). In some embodiments,
each type of input may be associated with a different action. For
example, a tap may be associated with the function of taking a
picture, while a right-to-left slide may be associated with the
function of recording a video.
[0138] Apparatus 110 may be attached to an article of clothing
(e.g., a shirt, a belt, pants, etc.), of user 100 at an edge of the
clothing using a clip 431 as shown in FIG. 4C. For example, the
body of apparatus 100 may reside adjacent to the inside surface of
the clothing with clip 431 engaging with the outside surface of the
clothing. In such an embodiment, as shown in FIG. 4C, the image
sensor 220 (e.g., a camera for visible light) may be protruding
beyond the edge of the clothing. Alternatively, clip 431 may be
engaging with the inside surface of the clothing with the body of
apparatus 110 being adjacent to the outside of the clothing. In
various embodiments, the clothing may be positioned between clip
431 and the body of apparatus 110.
[0139] An example embodiment of apparatus 110 is shown in FIG. 4D.
Apparatus 110 includes clip 431 which may include points (e.g.,
432A and 432B) in close proximity to a front surface 434 of a body
435 of apparatus 110. In an example embodiment, the distance
between points 432A, 432B and front surface 434 may be less than a
typical thickness of a fabric of the clothing of user 100. For
example, the distance between points 432A, 432B and surface 434 may
be less than a thickness of a tee-shirt, e.g., less than a
millimeter, less than 2 millimeters, less than 3 millimeters, etc.,
or, in some cases, points 432A, 432B of clip 431 may touch surface
434. In various embodiments, clip 431 may include a point 433 that
does not touch surface 434, allowing the clothing to be inserted
between clip 431 and surface 434.
[0140] FIG. 4D shows schematically different views of apparatus 110
defined as a front view (F-view), a rearview (R-view), a top view
(T-view), a side view (S-view) and a bottom view (B-view). These
views will be referred to when describing apparatus 110 in
subsequent figures. FIG. 4D shows an example embodiment where clip
431 is positioned at the same side of apparatus 110 as sensor 220
(e.g., the front side of apparatus 110). Alternatively, clip 431
may be positioned at an opposite side of apparatus 110 as sensor
220 (e.g., the rear side of apparatus 110). In various embodiments,
apparatus 110 may include function button 430, as shown in FIG.
4D.
[0141] Various views of apparatus 110 are illustrated in FIGS. 4E
through 4K. For example, FIG. 4E shows a view of apparatus 110 with
an electrical connection 441. Electrical connection 441 may be, for
example, a USB port, that may be used to transfer data to/from
apparatus 110 and provide electrical power to apparatus 110. In an
example embodiment, connection 441 may be used to charge a battery
442 schematically shown in FIG. 4E. FIG. 4F shows F-view of
apparatus 110, including sensor 220 and one or more microphones
443. In some embodiments, apparatus 110 may include several
microphones 443 facing outwards, wherein microphones 443 are
configured to obtain environmental sounds and sounds of various
speakers communicating with user 100. FIG. 4G shows R-view of
apparatus 110. In some embodiments, microphone 444 may be
positioned at the rear side of apparatus 110, as shown in FIG. 4G.
Microphone 444 may be used to detect an audio signal from user 100.
It should be noted, that apparatus 110 may have microphones placed
at any side (e.g., a front side, a rear side, a left side, a right
side, a top side, or a bottom side) of apparatus 110. In various
embodiments, some microphones may be at a first side (e.g.,
microphones 443 may be at the front of apparatus 110) and other
microphones may be at a second side (e.g., microphone 444 may be at
the back side of apparatus 110).
[0142] FIGS. 4H and 41 show different sides of apparatus 110 (i.e.,
S-view of apparatus 110) consisted with disclosed embodiments. For
example, FIG. 4H shows the location of sensor 220 and an example
shape of clip 431. FIG. 4J shows T-view of apparatus 110, including
function button 430, and FIG. 4K shows B-view of apparatus 110 with
electrical connection 441.
[0143] The example embodiments discussed above with respect to
FIGS. 3A, 3B, 4A, and 4B are not limiting. In some embodiments,
apparatus 110 may be implemented in any suitable configuration for
performing the disclosed methods. For example, referring back to
FIG. 2, the disclosed embodiments may implement an apparatus 110
according to any configuration including an image sensor 220 and a
processor unit 210 to perform image analysis and for communicating
with a feedback unit 230.
[0144] FIG. 5A is a block diagram illustrating the components of
apparatus 110 according to an example embodiment. As shown in FIG.
5A, and as similarly discussed above, apparatus 110 includes an
image sensor 220, a memory 550, a processor 210, a feedback
outputting unit 230, a wireless transceiver 530, and a mobile power
source 520. In other embodiments, apparatus 110 may also include
buttons, other sensors such as a microphone, and inertial
measurements devices such as accelerometers, gyroscopes,
magnetometers, temperature sensors, color sensors, light sensors,
etc. Apparatus 110 may further include a data port 570 and a power
connection 510 with suitable interfaces for connecting with an
external power source or an external device (not shown).
[0145] Processor 210, depicted in FIG. 5A, may include any suitable
processing device. The term "processing device" includes any
physical device having an electric circuit that performs a logic
operation on input or inputs. For example, processing device may
include one or more integrated circuits, microchips,
microcontrollers, microprocessors, all or part of a central
processing unit (CPU), graphics processing unit (GPU), digital
signal processor (DSP), field-programmable gate array (FPGA), or
other circuits suitable for executing instructions or performing
logic operations. The instructions executed by the processing
device may, for example, be pre-loaded into a memory integrated
with or embedded into the processing device or may be stored in a
separate memory (e.g., memory 550). Memory 550 may comprise a
Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk,
an optical disk, a magnetic medium, a flash memory, other
permanent, fixed, or volatile memory, or any other mechanism
capable of storing instructions.
[0146] Although, in the embodiment illustrated in FIG. 5A,
apparatus 110 includes one processing device (e.g., processor 210),
apparatus 110 may include more than one processing device. Each
processing device may have a similar construction, or the
processing devices may be of differing constructions that are
electrically connected or disconnected from each other. For
example, the processing devices may be separate circuits or
integrated in a single circuit. When more than one processing
device is used, the processing devices may be configured to operate
independently or collaboratively. The processing devices may be
coupled electrically, magnetically, optically, acoustically,
mechanically or by other means that permit them to interact.
[0147] In some embodiments, processor 210 may process a plurality
of images captured from the environment of user 100 to determine
different parameters related to capturing subsequent images. For
example, processor 210 can determine, based on information derived
from captured image data, a value for at least one of the
following: an image resolution, a compression ratio, a cropping
parameter, frame rate, a focus point, an exposure time, an aperture
size, and a light sensitivity. The determined value may be used in
capturing at least one subsequent image. Additionally, processor
210 can detect images including at least one hand-related trigger
in the environment of the user and perform an action and/or provide
an output of information to a user via feedback outputting unit
230.
[0148] In another embodiment, processor 210 can change the aiming
direction of image sensor 220. For example, when apparatus 110 is
attached with clip 420, the aiming direction of image sensor 220
may not coincide with the field-of-view of user 100. Processor 210
may recognize certain situations from the analyzed image data and
adjust the aiming direction of image sensor 220 to capture relevant
image data. For example, in one embodiment, processor 210 may
detect an interaction with another individual and sense that the
individual is not fully in view, because image sensor 220 is tilted
down. Responsive thereto, processor 210 may adjust the aiming
direction of image sensor 220 to capture image data of the
individual. Other scenarios are also contemplated where processor
210 may recognize the need to adjust an aiming direction of image
sensor 220.
[0149] In some embodiments, processor 210 may communicate data to
feedback-outputting unit 230, which may include any device
configured to provide information to a user 100. Feedback
outputting unit 230 may be provided as part of apparatus 110 (as
shown) or may be provided external to apparatus 110 and
communicatively coupled thereto. Feedback-outputting unit 230 may
be configured to output visual or nonvisual feedback based on
signals received from processor 210, such as when processor 210
recognizes a hand-related trigger in the analyzed image data.
[0150] The term "feedback" refers to any output or information
provided in response to processing at least one image in an
environment. In some embodiments, as similarly described above,
feedback may include an audible or visible indication of time
information, detected text or numerals, the value of currency, a
branded product, a person's identity, the identity of a landmark or
other environmental situation or condition including the street
names at an intersection or the color of a traffic light, etc., as
well as other information associated with each of these. For
example, in some embodiments, feedback may include additional
information regarding the amount of currency still needed to
complete a transaction, information regarding the identified
person, historical information or times and prices of admission
etc. of a detected landmark etc. In some embodiments, feedback may
include an audible tone, a tactile response, and/or information
previously recorded by user 100. Feedback-outputting unit 230 may
comprise appropriate components for outputting acoustical and
tactile feedback. For example, feedback-outputting unit 230 may
comprise audio headphones, a hearing aid type device, a speaker, a
bone conduction headphone, interfaces that provide tactile cues,
vibrotactile stimulators, etc. In some embodiments, processor 210
may communicate signals with an external feedback outputting unit
230 via a wireless transceiver 530, a wired connection, or some
other communication interface. In some embodiments, feedback
outputting unit 230 may also include any suitable display device
for visually displaying information to user 100.
[0151] As shown in FIG. 5A, apparatus 110 includes memory 550.
Memory 550 may include one or more sets of instructions accessible
to processor 210 to perform the disclosed methods, including
instructions for recognizing a hand-related trigger in the image
data. In some embodiments memory 550 may store image data (e.g.,
images, videos) captured from the environment of user 100. In
addition, memory 550 may store information specific to user 100,
such as image representations of known individuals, favorite
products, personal items, and calendar or appointment information,
etc. In some embodiments, processor 210 may determine, for example,
which type of image data to store based on available storage space
in memory 550. In another embodiment, processor 210 may extract
information from the image data stored in memory 550.
[0152] As further shown in FIG. 5A, apparatus 110 includes mobile
power source 520. The term "mobile power source" includes any
device capable of providing electrical power, which can be easily
carried by hand (e.g., mobile power source 520 may weigh less than
a pound). The mobility of the power source enables user 100 to use
apparatus 110 in a variety of situations. In some embodiments,
mobile power source 520 may include one or more batteries (e.g.,
nickel-cadmium batteries, nickel-metal hydride batteries, and
lithium-ion batteries) or any other type of electrical power
supply. In other embodiments, mobile power source 520 may be
rechargeable and contained within a casing that holds apparatus
110. In yet other embodiments, mobile power source 520 may include
one or more energy harvesting devices for converting ambient energy
into electrical energy (e.g., portable solar power units, human
vibration units, etc.).
[0153] Mobile power source 520 may power one or more wireless
transceivers (e.g., wireless transceiver 530 in FIG. 5A). The term
"wireless transceiver" refers to any device configured to exchange
transmissions over an air interface by use of radio frequency,
infrared frequency, magnetic field, or electric field. Wireless
transceiver 530 may use any known standard to transmit and/or
receive data (e.g., Wi-Fi, Bluetooth.RTM., Bluetooth Smart,
802.15.4, or ZigBee). In some embodiments, wireless transceiver 530
may transmit data (e.g., raw image data, processed image data,
extracted information) from apparatus 110 to computing device 120
and/or server 250. Wireless transceiver 530 may also receive data
from computing device 120 and/or server 250. In other embodiments,
wireless transceiver 530 may transmit data and instructions to an
external feedback outputting unit 230.
[0154] FIG. 5B is a block diagram illustrating the components of
apparatus 110 according to another example embodiment. In some
embodiments, apparatus 110 includes a first image sensor 220a, a
second image sensor 220b, a memory 550, a first processor 210a, a
second processor 210b, a feedback outputting unit 230, a wireless
transceiver 530, a mobile power source 520, and a power connector
510. In the arrangement shown in FIG. 5B, each of the image sensors
may provide images in a different image resolution, or face a
different direction. Alternatively, each image sensor may be
associated with a different camera (e.g., a wide angle camera, a
narrow angle camera, an IR camera, etc.). In some embodiments,
apparatus 110 can select which image sensor to use based on various
factors. For example, processor 210a may determine, based on
available storage space in memory 550, to capture subsequent images
in a certain resolution.
[0155] Apparatus 110 may operate in a first processing-mode and in
a second processing-mode, such that the first processing-mode may
consume less power than the second processing-mode. For example, in
the first processing-mode, apparatus 110 may capture images and
process the captured images to make real-time decisions based on an
identifying hand-related trigger, for example. In the second
processing-mode, apparatus 110 may extract information from stored
images in memory 550 and delete images from memory 550. In some
embodiments, mobile power source 520 may provide more than fifteen
hours of processing in the first processing-mode and about three
hours of processing in the second processing-mode. Accordingly,
different processing-modes may allow mobile power source 520 to
produce sufficient power for powering apparatus 110 for various
time periods (e.g., more than two hours, more than four hours, more
than ten hours, etc.).
[0156] In some embodiments, apparatus 110 may use first processor
210a in the first processing-mode when powered by mobile power
source 520, and second processor 210b in the second processing-mode
when powered by external power source 580 that is connectable via
power connector 510. In other embodiments, apparatus 110 may
determine, based on predefined conditions, which processors or
which processing modes to use. Apparatus 110 may operate in the
second processing-mode even when apparatus 110 is not powered by
external power source 580. For example, apparatus 110 may determine
that it should operate in the second processing-mode when apparatus
110 is not powered by external power source 580, if the available
storage space in memory 550 for storing new image data is lower
than a predefined threshold.
[0157] Although one wireless transceiver is depicted in FIG. 5B,
apparatus 110 may include more than one wireless transceiver (e.g.,
two wireless transceivers). In an arrangement with more than one
wireless transceiver, each of the wireless transceivers may use a
different standard to transmit and/or receive data. In some
embodiments, a first wireless transceiver may communicate with
server 250 or computing device 120 using a cellular standard (e.g.,
LTE or GSM), and a second wireless transceiver may communicate with
server 250 or computing device 120 using a short-range standard
(e.g., Wi-Fi or Bluetooth.RTM.). In some embodiments, apparatus 110
may use the first wireless transceiver when the wearable apparatus
is powered by a mobile power source included in the wearable
apparatus, and use the second wireless transceiver when the
wearable apparatus is powered by an external power source.
[0158] FIG. 5C is a block diagram illustrating the components of
apparatus 110 according to another example embodiment including
computing device 120. In this embodiment, apparatus 110 includes an
image sensor 220, a memory 550a, a first processor 210, a
feedback-outputting unit 230, a wireless transceiver 530a, a mobile
power source 520, and a power connector 510. As further shown in
FIG. 5C, computing device 120 includes a processor 540, a
feedback-outputting unit 545, a memory 550b, a wireless transceiver
530b, and a display 260. One example of computing device 120 is a
smartphone or tablet having a dedicated application installed
therein. In other embodiments, computing device 120 may include any
configuration such as an on-board automobile computing system, a
PC, a laptop, and any other system consistent with the disclosed
embodiments. In this example, user 100 may view feedback output in
response to identification of a hand-related trigger on display
260. Additionally, user 100 may view other data (e.g., images,
video clips, object information, schedule information, extracted
information, etc.) on display 260. In addition, user 100 may
communicate with server 250 via computing device 120.
[0159] In some embodiments, processor 210 and processor 540 are
configured to extract information from captured image data. The
term "extracting information" includes any process by which
information associated with objects, individuals, locations,
events, etc., is identified in the captured image data by any means
known to those of ordinary skill in the art. In some embodiments,
apparatus 110 may use the extracted information to send feedback or
other real-time indications to feedback outputting unit 230 or to
computing device 120. In some embodiments, processor 210 may
identify in the image data the individual standing in front of user
100, and send computing device 120 the name of the individual and
the last time user 100 met the individual. In another embodiment,
processor 210 may identify in the image data, one or more visible
triggers, including a hand-related trigger, and determine whether
the trigger is associated with a person other than the user of the
wearable apparatus to selectively determine whether to perform an
action associated with the trigger. One such action may be to
provide a feedback to user 100 via feedback-outputting unit 230
provided as part of (or in communication with) apparatus 110 or via
a feedback unit 545 provided as part of computing device 120. For
example, feedback-outputting unit 545 may be in communication with
display 260 to cause the display 260 to visibly output information.
In some embodiments, processor 210 may identify in the image data a
hand-related trigger and send computing device 120 an indication of
the trigger. Processor 540 may then process the received trigger
information and provide an output via feedback outputting unit 545
or display 260 based on the hand-related trigger. In other
embodiments, processor 540 may determine a hand-related trigger and
provide suitable feedback similar to the above, based on image data
received from apparatus 110. In some embodiments, processor 540 may
provide instructions or other information, such as environmental
information to apparatus 110 based on an identified hand-related
trigger.
[0160] In some embodiments, processor 210 may identify other
environmental information in the analyzed images, such as an
individual standing in front user 100, and send computing device
120 information related to the analyzed information such as the
name of the individual and the last time user 100 met the
individual. In a different embodiment, processor 540 may extract
statistical information from captured image data and forward the
statistical information to server 250. For example, certain
information regarding the types of items a user purchases, or the
frequency a user patronizes a particular merchant, etc. may be
determined by processor 540. Based on this information, server 250
may send computing device 120 coupons and discounts associated with
the user's preferences.
[0161] When apparatus 110 is connected or wirelessly connected to
computing device 120, apparatus 110 may transmit at least part of
the image data stored in memory 550a for storage in memory 550b. In
some embodiments, after computing device 120 confirms that
transferring the part of image data was successful, processor 540
may delete the part of the image data. The term "delete" means that
the image is marked as `deleted` and other image data may be stored
instead of it, but does not necessarily mean that the image data
was physically removed from the memory.
[0162] As will be appreciated by a person skilled in the art having
the benefit of this disclosure, numerous variations and/or
modifications may be made to the disclosed embodiments. Not all
components are essential for the operation of apparatus 110. Any
component may be located in any appropriate apparatus and the
components may be rearranged into a variety of configurations while
providing the functionality of the disclosed embodiments. For
example, in some embodiments, apparatus 110 may include a camera, a
processor, and a wireless transceiver for sending data to another
device. Therefore, the foregoing configurations are examples and,
regardless of the configurations discussed above, apparatus 110 can
capture, store, and/or process images.
[0163] Further, the foregoing and following description refers to
storing and/or processing images or image data. In the embodiments
disclosed herein, the stored and/or processed images or image data
may comprise a representation of one or more images captured by
image sensor 220. As the term is used herein, a "representation" of
an image (or image data) may include an entire image or a portion
of an image. A representation of an image (or image data) may have
the same resolution or a lower resolution as the image (or image
data), and/or a representation of an image (or image data) may be
altered in some respect (e.g., be compressed, have a lower
resolution, have one or more colors that are altered, etc.).
[0164] For example, apparatus 110 may capture an image and store a
representation of the image that is compressed as a .JPG file. As
another example, apparatus 110 may capture an image in color, but
store a black-and-white representation of the color image. As yet
another example, apparatus 110 may capture an image and store a
different representation of the image (e.g., a portion of the
image). For example, apparatus 110 may store a portion of an image
that includes a face of a person who appears in the image, but that
does not substantially include the environment surrounding the
person. Similarly, apparatus 110 may, for example, store a portion
of an image that includes a product that appears in the image, but
does not substantially include the environment surrounding the
product. As yet another example, apparatus 110 may store a
representation of an image at a reduced resolution (i.e., at a
resolution that is of a lower value than that of the captured
image). Storing representations of images may allow apparatus 110
to save storage space in memory 550. Furthermore, processing
representations of images may allow apparatus 110 to improve
processing efficiency and/or help to preserve battery life.
[0165] In addition to the above, in some embodiments, any one of
apparatus 110 or computing device 120, via processor 210 or 540,
may further process the captured image data to provide additional
functionality to recognize objects and/or gestures and/or other
information in the captured image data. In some embodiments,
actions may be taken based on the identified objects, gestures, or
other information. In some embodiments, processor 210 or 540 may
identify in the image data, one or more visible triggers, including
a hand-related trigger, and determine whether the trigger is
associated with a person other than the user to determine whether
to perform an action associated with the trigger.
[0166] Some embodiments of the present disclosure may include an
apparatus securable to an article of clothing of a user. Such an
apparatus may include two portions, connectable by a connector. A
capturing unit may be designed to be worn on the outside of a
user's clothing, and may include an image sensor for capturing
images of a user's environment. The capturing unit may be connected
to or connectable to a power unit, which may be configured to house
a power source and a processing device. The capturing unit may be a
small device including a camera or other device for capturing
images. The capturing unit may be designed to be inconspicuous and
unobtrusive, and may be configured to communicate with a power unit
concealed by a user's clothing. The power unit may include bulkier
aspects of the system, such as transceiver antennas, at least one
battery, a processing device, etc. In some embodiments,
communication between the capturing unit and the power unit may be
provided by a data cable included in the connector, while in other
embodiments, communication may be wirelessly achieved between the
capturing unit and the power unit. Some embodiments may permit
alteration of the orientation of an image sensor of the capture
unit, for example to better capture images of interest.
[0167] FIG. 6 illustrates an exemplary embodiment of a memory
containing software modules consistent with the present disclosure.
Included in memory 550 are orientation identification module 601,
orientation adjustment module 602, and motion tracking module 603.
Modules 601, 602, 603 may contain software instructions for
execution by at least one processing device, e.g., processor 210,
included with a wearable apparatus. Orientation identification
module 601, orientation adjustment module 602, and motion tracking
module 603 may cooperate to provide orientation adjustment for a
capturing unit incorporated into wireless apparatus 110.
[0168] FIG. 7 illustrates an exemplary capturing unit 710 including
an orientation adjustment unit 705. Orientation adjustment unit 705
may be configured to permit the adjustment of image sensor 220. As
illustrated in FIG. 7, orientation adjustment unit 705 may include
an eye-ball type adjustment mechanism. In alternative embodiments,
orientation adjustment unit 705 may include gimbals, adjustable
stalks, pivotable mounts, and any other suitable unit for adjusting
an orientation of image sensor 220.
[0169] Image sensor 220 may be configured to be movable with the
head of user 100 in such a manner that an aiming direction of image
sensor 220 substantially coincides with a field of view of user
100. For example, as described above, a camera associated with
image sensor 220 may be installed within capturing unit 710 at a
predetermined angle in a position facing slightly upwards or
downwards, depending on an intended location of capturing unit 710.
Accordingly, the set aiming direction of image sensor 220 may match
the field-of-view of user 100. In some embodiments, processor 210
may change the orientation of image sensor 220 using image data
provided from image sensor 220. For example, processor 210 may
recognize that a user is reading a book and determine that the
aiming direction of image sensor 220 is offset from the text. That
is, because the words in the beginning of each line of text are not
fully in view, processor 210 may determine that image sensor 220 is
tilted in the wrong direction. Responsive thereto, processor 210
may adjust the aiming direction of image sensor 220.
[0170] Orientation identification module 601 may be configured to
identify an orientation of an image sensor 220 of capturing unit
710. An orientation of an image sensor 220 may be identified, for
example, by analysis of images captured by image sensor 220 of
capturing unit 710, by tilt or attitude sensing devices within
capturing unit 710, and by measuring a relative direction of
orientation adjustment unit 705 with respect to the remainder of
capturing unit 710.
[0171] Orientation adjustment module 602 may be configured to
adjust an orientation of image sensor 220 of capturing unit 710. As
discussed above, image sensor 220 may be mounted on an orientation
adjustment unit 705 configured for movement. Orientation adjustment
unit 705 may be configured for rotational and/or lateral movement
in response to commands from orientation adjustment module 602. In
some embodiments orientation adjustment unit 705 may be adjust an
orientation of image sensor 220 via motors, electromagnets,
permanent magnets, and/or any suitable combination thereof.
[0172] In some embodiments, monitoring module 603 may be provided
for continuous monitoring. Such continuous monitoring may include
tracking a movement of at least a portion of an object included in
one or more images captured by the image sensor. For example, in
one embodiment, apparatus 110 may track an object as long as the
object remains substantially within the field-of-view of image
sensor 220. In additional embodiments, monitoring module 603 may
engage orientation adjustment module 602 to instruct orientation
adjustment unit 705 to continually orient image sensor 220 towards
an object of interest. For example, in one embodiment, monitoring
module 603 may cause image sensor 220 to adjust an orientation to
ensure that a certain designated object, for example, the face of a
particular person, remains within the field-of view of image sensor
220, even as that designated object moves about. In another
embodiment, monitoring module 603 may continuously monitor an area
of interest included in one or more images captured by the image
sensor. For example, a user may be occupied by a certain task, for
example, typing on a laptop, while image sensor 220 remains
oriented in a particular direction and continuously monitors a
portion of each image from a series of images to detect a trigger
or other event. For example, image sensor 210 may be oriented
towards a piece of laboratory equipment and monitoring module 603
may be configured to monitor a status light on the laboratory
equipment for a change in status, while the user's attention is
otherwise occupied.
[0173] In some embodiments consistent with the present disclosure,
capturing unit 710 may include a plurality of image sensors 220.
The plurality of image sensors 220 may each be configured to
capture different image data. For example, when a plurality of
image sensors 220 are provided, the image sensors 220 may capture
images having different resolutions, may capture wider or narrower
fields of view, and may have different levels of magnification.
Image sensors 220 may be provided with varying lenses to permit
these different configurations. In some embodiments, a plurality of
image sensors 220 may include image sensors 220 having different
orientations. Thus, each of the plurality of image sensors 220 may
be pointed in a different direction to capture different images.
The fields of view of image sensors 220 may be overlapping in some
embodiments. The plurality of image sensors 220 may each be
configured for orientation adjustment, for example, by being paired
with an image adjustment unit 705. In some embodiments, monitoring
module 603, or another module associated with memory 550, may be
configured to individually adjust the orientations of the plurality
of image sensors 220 as well as to turn each of the plurality of
image sensors 220 on or off as may be required. In some
embodiments, monitoring an object or person captured by an image
sensor 220 may include tracking movement of the object across the
fields of view of the plurality of image sensors 220.
[0174] Embodiments consistent with the present disclosure may
include connectors configured to connect a capturing unit and a
power unit of a wearable apparatus. Capturing units consistent with
the present disclosure may include least one image sensor
configured to capture images of an environment of a user. Power
units consistent with the present disclosure may be configured to
house a power source and/or at least one processing device.
Connectors consistent with the present disclosure may be configured
to connect the capturing unit and the power unit, and may be
configured to secure the apparatus to an article of clothing such
that the capturing unit is positioned over an outer surface of the
article of clothing and the power unit is positioned under an inner
surface of the article of clothing. Exemplary embodiments of
capturing units, connectors, and power units consistent with the
disclosure are discussed in further detail with respect to FIGS.
8-14.
[0175] FIG. 8 is a schematic illustration of an embodiment of
wearable apparatus 110 securable to an article of clothing
consistent with the present disclosure. As illustrated in FIG. 8,
capturing unit 710 and power unit 720 may be connected by a
connector 730 such that capturing unit 710 is positioned on one
side of an article of clothing 750 and power unit 720 is positioned
on the opposite side of the clothing 750. In some embodiments,
capturing unit 710 may be positioned over an outer surface of the
article of clothing 750 and power unit 720 may be located under an
inner surface of the article of clothing 750. The power unit 720
may be configured to be placed against the skin of a user.
[0176] Capturing unit 710 may include an image sensor 220 and an
orientation adjustment unit 705 (as illustrated in FIG. 7). Power
unit 720 may include mobile power source 520 and processor 210.
Power unit 720 may further include any combination of elements
previously discussed that may be a part of wearable apparatus 110,
including, but not limited to, wireless transceiver 530, feedback
outputting unit 230, memory 550, and data port 570.
[0177] Connector 730 may include a clip 715 or other mechanical
connection designed to clip or attach capturing unit 710 and power
unit 720 to an article of clothing 750 as illustrated in FIG. 8. As
illustrated, clip 715 may connect to each of capturing unit 710 and
power unit 720 at a perimeter thereof, and may wrap around an edge
of the article of clothing 750 to affix the capturing unit 710 and
power unit 720 in place. Connector 730 may further include a power
cable 760 and a data cable 770. Power cable 760 may be capable of
conveying power from mobile power source 520 to image sensor 220 of
capturing unit 710. Power cable 760 may also be configured to
provide power to any other elements of capturing unit 710, e.g.,
orientation adjustment unit 705. Data cable 770 may be capable of
conveying captured image data from image sensor 220 in capturing
unit 710 to processor 800 in the power unit 720. Data cable 770 may
be further capable of conveying additional data between capturing
unit 710 and processor 800, e.g., control instructions for
orientation adjustment unit 705.
[0178] FIG. 9 is a schematic illustration of a user 100 wearing a
wearable apparatus 110 consistent with an embodiment of the present
disclosure. As illustrated in FIG. 9, capturing unit 710 is located
on an exterior surface of the clothing 750 of user 100. Capturing
unit 710 is connected to power unit 720 (not seen in this
illustration) via connector 730, which wraps around an edge of
clothing 750.
[0179] In some embodiments, connector 730 may include a flexible
printed circuit board (PCB). FIG. 10 illustrates an exemplary
embodiment wherein connector 730 includes a flexible printed
circuit board 765. Flexible printed circuit board 765 may include
data connections and power connections between capturing unit 710
and power unit 720. Thus, in some embodiments, flexible printed
circuit board 765 may serve to replace power cable 760 and data
cable 770. In alternative embodiments, flexible printed circuit
board 765 may be included in addition to at least one of power
cable 760 and data cable 770. In various embodiments discussed
herein, flexible printed circuit board 765 may be substituted for,
or included in addition to, power cable 760 and data cable 770.
[0180] FIG. 11 is a schematic illustration of another embodiment of
a wearable apparatus securable to an article of clothing consistent
with the present disclosure. As illustrated in FIG. 11, connector
730 may be centrally located with respect to capturing unit 710 and
power unit 720. Central location of connector 730 may facilitate
affixing apparatus 110 to clothing 750 through a hole in clothing
750 such as, for example, a button-hole in an existing article of
clothing 750 or a specialty hole in an article of clothing 750
designed to accommodate wearable apparatus 110.
[0181] FIG. 12 is a schematic illustration of still another
embodiment of wearable apparatus 110 securable to an article of
clothing. As illustrated in FIG. 12, connector 730 may include a
first magnet 731 and a second magnet 732. First magnet 731 and
second magnet 732 may secure capturing unit 710 to power unit 720
with the article of clothing positioned between first magnet 731
and second magnet 732. In embodiments including first magnet 731
and second magnet 732, power cable 760 and data cable 770 may also
be included. In these embodiments, power cable 760 and data cable
770 may be of any length, and may provide a flexible power and data
connection between capturing unit 710 and power unit 720.
Embodiments including first magnet 731 and second magnet 732 may
further include a flexible PCB 765 connection in addition to or
instead of power cable 760 and/or data cable 770. In some
embodiments, first magnet 731 or second magnet 732 may be replaced
by an object comprising a metal material.
[0182] FIG. 13 is a schematic illustration of yet another
embodiment of a wearable apparatus 110 securable to an article of
clothing. FIG. 13 illustrates an embodiment wherein power and data
may be wirelessly transferred between capturing unit 710 and power
unit 720. As illustrated in FIG. 13, first magnet 731 and second
magnet 732 may be provided as connector 730 to secure capturing
unit 710 and power unit 720 to an article of clothing 750. Power
and/or data may be transferred between capturing unit 710 and power
unit 720 via any suitable wireless technology, for example,
magnetic and/or capacitive coupling, near field communication
technologies, radiofrequency transfer, and any other wireless
technology suitable for transferring data and/or power across short
distances.
[0183] FIG. 14 illustrates still another embodiment of wearable
apparatus 110 securable to an article of clothing 750 of a user. As
illustrated in FIG. 14, connector 730 may include features designed
for a contact fit. For example, capturing unit 710 may include a
ring 733 with a hollow center having a diameter slightly larger
than a disk-shaped protrusion 734 located on power unit 720. When
pressed together with fabric of an article of clothing 750 between
them, disk-shaped protrusion 734 may fit tightly inside ring 733,
securing capturing unit 710 to power unit 720. FIG. 14 illustrates
an embodiment that does not include any cabling or other physical
connection between capturing unit 710 and power unit 720. In this
embodiment, capturing unit 710 and power unit 720 may transfer
power and data wirelessly. In alternative embodiments, capturing
unit 710 and power unit 720 may transfer power and data via at
least one of cable 760, data cable 770, and flexible printed
circuit board 765.
[0184] FIG. 15 illustrates another aspect of power unit 720
consistent with embodiments described herein. Power unit 720 may be
configured to be positioned directly against the user's skin. To
facilitate such positioning, power unit 720 may further include at
least one surface coated with a biocompatible material 740.
Biocompatible materials 740 may include materials that will not
negatively react with the skin of the user when worn against the
skin for extended periods of time. Such materials may include, for
example, silicone, PTFE, kapton, polyimide, titanium, nitinol,
platinum, and others. Also as illustrated in FIG. 15, power unit
720 may be sized such that an inner volume of the power unit is
substantially filled by mobile power source 520. That is, in some
embodiments, the inner volume of power unit 720 may be such that
the volume does not accommodate any additional components except
for mobile power source 520. In some embodiments, mobile power
source 520 may take advantage of its close proximity to the skin of
user's skin. For example, mobile power source 520 may use the
Peltier effect to produce power and/or charge the power source.
[0185] In further embodiments, an apparatus securable to an article
of clothing may further include protective circuitry associated
with power source 520 housed in in power unit 720. FIG. 16
illustrates an exemplary embodiment including protective circuitry
775. As illustrated in FIG. 16, protective circuitry 775 may be
located remotely with respect to power unit 720. In alternative
embodiments, protective circuitry 775 may also be located in
capturing unit 710, on flexible printed circuit board 765, or in
power unit 720.
[0186] Protective circuitry 775 may be configured to protect image
sensor 220 and/or other elements of capturing unit 710 from
potentially dangerous currents and/or voltages produced by mobile
power source 520. Protective circuitry 775 may include passive
components such as capacitors, resistors, diodes, inductors, etc.,
to provide protection to elements of capturing unit 710. In some
embodiments, protective circuitry 775 may also include active
components, such as transistors, to provide protection to elements
of capturing unit 710. For example, in some embodiments, protective
circuitry 775 may comprise one or more resistors serving as fuses.
Each fuse may comprise a wire or strip that melts (thereby braking
a connection between circuitry of image capturing unit 710 and
circuitry of power unit 720) when current flowing through the fuse
exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps,
1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.) Any or all of the
previously described embodiments may incorporate protective
circuitry 775.
[0187] In some embodiments, the wearable apparatus may transmit
data to a computing device (e.g., a smartphone, tablet, watch,
computer, etc.) over one or more networks via any known wireless
standard (e.g., cellular, Wi-Fi, Bluetooth.RTM., etc.), or via
near-filed capacitive coupling, other short range wireless
techniques, or via a wired connection. Similarly, the wearable
apparatus may receive data from the computing device over one or
more networks via any known wireless standard (e.g., cellular,
Wi-Fi, Bluetooth.RTM., etc.), or via near-filed capacitive
coupling, other short range wireless techniques, or via a wired
connection. The data transmitted to the wearable apparatus and/or
received by the wireless apparatus may include images, portions of
images, identifiers related to information appearing in analyzed
images or associated with analyzed audio, or any other data
representing image and/or audio data. For example, an image may be
analyzed and an identifier related to an activity occurring in the
image may be transmitted to the computing device (e.g., the "paired
device"). In the embodiments described herein, the wearable
apparatus may process images and/or audio locally (on board the
wearable apparatus) and/or remotely (via a computing device).
Further, in the embodiments described herein, the wearable
apparatus may transmit data related to the analysis of images
and/or audio to a computing device for further analysis, display,
and/or transmission to another device (e.g., a paired device).
Further, a paired device may execute one or more applications
(apps) to process, display, and/or analyze data (e.g., identifiers,
text, images, audio, etc.) received from the wearable
apparatus.
[0188] Some of the disclosed embodiments may involve systems,
devices, methods, and software products for determining at least
one keyword. For example, at least one keyword may be determined
based on data collected by apparatus 110. At least one search query
may be determined based on the at least one keyword. The at least
one search query may be transmitted to a search engine.
[0189] In some embodiments, at least one keyword may be determined
based on at least one or more images captured by image sensor 220.
In some cases, the at least one keyword may be selected from a
keywords pool stored in memory. In some cases, optical character
recognition (OCR) may be performed on at least one image captured
by image sensor 220, and the at least one keyword may be determined
based on the OCR result. In some cases, at least one image captured
by image sensor 220 may be analyzed to recognize: a person, an
object, a location, a scene, and so forth. Further, the at least
one keyword may be determined based on the recognized person,
object, location, scene, etc. For example, the at least one keyword
may comprise: a person's name, an object's name, a place's name, a
date, a sport team's name, a movie's name, a book's name, and so
forth.
[0190] In some embodiments, at least one keyword may be determined
based on the user's behavior. The user's behavior may be determined
based on an analysis of the one or more images captured by image
sensor 220. In some embodiments, at least one keyword may be
determined based on activities of a user and/or other person. The
one or more images captured by image sensor 220 may be analyzed to
identify the activities of the user and/or the other person who
appears in one or more images captured by image sensor 220. In some
embodiments, at least one keyword may be determined based on at
least one or more audio segments captured by apparatus 110. In some
embodiments, at least one keyword may be determined based on at
least GPS information associated with the user. In some
embodiments, at least one keyword may be determined based on at
least the current time and/or date.
[0191] In some embodiments, at least one search query may be
determined based on at least one keyword. In some cases, the at
least one search query may comprise the at least one keyword. In
some cases, the at least one search query may comprise the at least
one keyword and additional keywords provided by the user. In some
cases, the at least one search query may comprise the at least one
keyword and one or more images, such as images captured by image
sensor 220. In some cases, the at least one search query may
comprise the at least one keyword and one or more audio segments,
such as audio segments captured by apparatus 110.
[0192] In some embodiments, the at least one search query may be
transmitted to a search engine. In some embodiments, search results
provided by the search engine in response to the at least one
search query may be provided to the user. In some embodiments, the
at least one search query may be used to access a database.
[0193] For example, in one embodiment, the keywords may include a
name of a type of food, such as quinoa, or a brand name of a food
product; and the search will output information related to
desirable quantities of consumption, facts about the nutritional
profile, and so forth. In another example, in one embodiment, the
keywords may include a name of a restaurant, and the search will
output information related to the restaurant, such as a menu,
opening hours, reviews, and so forth. The name of the restaurant
may be obtained using OCR on an image of signage, using GPS
information, and so forth. In another example, in one embodiment,
the keywords may include a name of a person, and the search will
provide information from a social network profile of the person.
The name of the person may be obtained using OCR on an image of a
name tag attached to the person's shirt, using face recognition
algorithms, and so forth. In another example, in one embodiment,
the keywords may include a name of a book, and the search will
output information related to the book, such as reviews, sales
statistics, information regarding the author of the book, and so
forth. In another example, in one embodiment, the keywords may
include a name of a movie, and the search will output information
related to the movie, such as reviews, box office statistics,
information regarding the cast of the movie, show times, and so
forth. In another example, in one embodiment, the keywords may
include a name of a sport team, and the search will output
information related to the sport team, such as statistics, latest
results, future schedule, information regarding the players of the
sport team, and so forth. For example, the name of the sport team
may be obtained using audio recognition algorithms.
[0194] Camera-Based Directional Hearing Aid
[0195] As discussed previously, the disclosed embodiments may
include providing feedback, such as acoustical and tactile
feedback, to one or more auxiliary devices in response to
processing at least one image in an environment. In some
embodiments, the auxiliary device may be an earpiece or other
device used to provide auditory feedback to the user, such as a
hearing aid. Traditional hearing aids often use microphones to
amplify sounds in the user's environment. These traditional
systems, however, are often unable to distinguish between sounds
that may be of particular importance to the wearer of the device,
or may do so on a limited basis. Using the systems and methods of
the disclosed embodiments, various improvements to traditional
hearing aids are provided, as described in detail below.
[0196] In one embodiment, a camera-based directional hearing aid
may be provided for selectively amplifying sounds based on a look
direction of a user. The hearing aid may communicate with an image
capturing device, such as apparatus 110, to determine the look
direction of the user. This look direction may be used to isolate
and/or selectively amplify sounds received from that direction
(e.g., sounds from individuals in the user's look direction, etc.).
Sounds received from directions other than the user's look
direction may be suppressed, attenuated, filtered or the like.
[0197] FIG. 17A is a schematic illustration of an example of a user
100 wearing an apparatus 110 for a camera-based hearing interface
device 1710 according to a disclosed embodiment. User 100 may wear
apparatus 110 that is physically connected to a shirt or other
piece of clothing of user 100, as shown. Consistent with the
disclosed embodiments, apparatus 110 may be positioned in other
locations, as described previously. For example, apparatus 110 may
be physically connected to a necklace, a belt, glasses, a wrist
strap, a button, etc. Apparatus 110 may be configured to
communicate with a hearing interface device such as hearing
interface device 1710. Such communication may be through a wired
connection, or may be made wirelessly (e.g., using a Bluetooth.TM.,
NFC, or forms of wireless communication). In some embodiments, one
or more additional devices may also be included, such as computing
device 120. Accordingly, one or more of the processes or functions
described herein with respect to apparatus 110 or processor 210 may
be performed by computing device 120 and/or processor 540.
[0198] Hearing interface device 1710 may be any device configured
to provide audible feedback to user 100. Hearing interface device
1710 may correspond to feedback outputting unit 230, described
above, and therefore any descriptions of feedback outputting unit
230 may also apply to hearing interface device 1710. In some
embodiments, hearing interface device 1710 may be separate from
feedback outputting unit 230 and may be configured to receive
signals from feedback outputting unit 230. As shown in FIG. 17A,
hearing interface device 1710 may be placed in one or both ears of
user 100, similar to traditional hearing interface devices. Hearing
interface device 1710 may be of various styles, including
in-the-canal, completely-in-canal, in-the-ear, behind-the-ear,
on-the-ear, receiver-in-canal, open fit, or various other styles.
Hearing interface device 1710 may include one or more speakers for
providing audible feedback to user 100, microphones for detecting
sounds in the environment of user 100, internal electronics,
processors, memories, etc. In some embodiments, in addition to or
instead of a microphone, hearing interface device 1710 may comprise
one or more communication units, and in particular one or more
receivers for receiving signals from apparatus 110 and transferring
the signals to user 100.
[0199] Hearing interface device 1710 may have various other
configurations or placement locations. In some embodiments, hearing
interface device 1710 may comprise a bone conduction headphone
1711, as shown in FIG. 17A. Bone conduction headphone 1711 may be
surgically implanted and may provide audible feedback to user 100
through bone conduction of sound vibrations to the inner ear.
Hearing interface device 1710 may also comprise one or more
headphones (e.g., wireless headphones, over-ear headphones, etc.)
or a portable speaker carried or worn by user 100. In some
embodiments, hearing interface device 1710 may be integrated into
other devices, such as a Bluetooth.TM. headset of the user,
glasses, a helmet (e.g., motorcycle helmets, bicycle helmets,
etc.), a hat, etc.
[0200] Apparatus 110 may be configured to determine a user look
direction 1750 of user 100. In some embodiments, user look
direction 1750 may be tracked by monitoring a direction of the
chin, or another body part or face part of user 100 relative to an
optical axis of a camera sensor 1751. Apparatus 110 may be
configured to capture one or more images of the surrounding
environment of user, for example, using image sensor 220. The
captured images may include a representation of a chin of user 100,
which may be used to determine user look direction 1750. Processor
210 (and/or processors 210a and 210b) may be configured to analyze
the captured images and detect the chin or another part of user 100
using various image detection or processing algorithms (e.g., using
convolutional neural networks (CNN), scale-invariant feature
transform (SIFT), histogram of oriented gradients (HOG) features,
or other techniques). Based on the detected representation of a
chin of user 100, look direction 1750 may be determined. Look
direction 1750 may be determined in part by comparing the detected
representation of a chin of user 100 to an optical axis of a camera
sensor 1751. For example, the optical axis 1751 may be known or
fixed in each image and processor 210 may determine look direction
1750 by comparing a representative angle of the chin of user 100 to
the direction of optical axis 1751. While the process is described
using a representation of a chin of user 100, various other
features may be detected for determining user look direction 1750,
including the user's face, nose, eyes, hand, etc.
[0201] In other embodiments, user look direction 1750 may be
aligned more closely with the optical axis 1751. For example, as
discussed above, apparatus 110 may be affixed to a pair of glasses
of user 100, as shown in FIG. 1A. In this embodiment, user look
direction 1750 may be the same as or close to the direction of
optical axis 1751. Accordingly, user look direction 1750 may be
determined or approximated based on the view of image sensor
220.
[0202] FIG. 17B is a schematic illustration of an embodiment of an
apparatus securable to an article of clothing consistent with the
present disclosure. Apparatus 110 may be securable to a piece of
clothing, such as the shirt of user 110, as shown in FIG. 17A.
Apparatus 110 may be securable to other articles of clothing, such
as a belt or pants of user 100, as discussed above. Apparatus 110
may have one or more cameras 1730, which may correspond to image
sensor 220. Camera 1730 may be configured to capture images of the
surrounding environment of user 100. In some embodiments, camera
1730 may be configured to detect a representation of a chin of the
user in the same images capturing the surrounding environment of
the user, which may be used for other functions described in this
disclosure. In other embodiments camera 1730 may be an auxiliary or
separate camera dedicated to determining user look direction
1750.
[0203] Apparatus 110 may further comprise one or more microphones
1720 for capturing sounds from the environment of user 100.
Microphone 1720 may also be configured to determine a
directionality of sounds in the environment of user 100. For
example, microphone 1720 may comprise one or more directional
microphones, which may be more sensitive to picking up sounds in
certain directions. For example, microphone 1720 may comprise a
unidirectional microphone, designed to pick up sound from a single
direction or small range of directions. Microphone 1720 may also
comprise a cardioid microphone, which may be sensitive to sounds
from the front and sides. Microphone 1720 may also include a
microphone array, which may comprise additional microphones, such
as microphone 1721 on the front of apparatus 110, or microphone
1722, placed on the side of apparatus 110. In some embodiments,
microphone 1720 may be a multi-port microphone for capturing
multiple audio signals. The microphones shown in FIG. 17B are by
way of example only, and any suitable number, configuration, or
location of microphones may be utilized. Processor 210 may be
configured to distinguish sounds within the environment of user 100
and determine an approximate directionality of each sound. For
example, using an array of microphones 1720, processor 210 may
compare the relative timing or amplitude of an individual sound
among the microphones 1720 to determine a directionality relative
to apparatus 100.
[0204] As a preliminary step before other audio analysis
operations, the sound captured from an environment of a user may be
classified using any audio classification technique. For example,
the sound may be classified into segments containing music, tones,
laughter, screams, or the like. Indications of the respective
segments may be logged in a database and may prove highly useful
for life logging applications. As one example, the logged
information may enable the system to to retrieve and/or determine a
mood when the user met another person. Additionally, such
processing is relatively fast and efficient, and does not require
significant computing resources, and transmitting the information
to a destination does not require significant bandwidth. Moreover,
once certain parts of the audio are classified as non-speech, more
computing resources may be available for processing the other
segments.
[0205] Based on the determined user look direction 1750, processor
210 may selectively condition or amplify sounds from a region
associated with user look direction 1750. FIG. 18 is a schematic
illustration showing an exemplary environment for use of a
camera-based hearing aid consistent with the present disclosure.
Microphone 1720 may detect one or more sounds 1820, 1821, and 1822
within the environment of user 100. Based on user look direction
1750, determined by processor 210, a region 1830 associated with
user look direction 1750 may be determined. As shown in FIG. 18,
region 1830 may be defined by a cone or range of directions based
on user look direction 1750. The range of angles may be defined by
an angle, .theta., as shown in FIG. 18. The angle, .theta., may be
any suitable angle for defining a range for conditioning sounds
within the environment of user 100 (e.g., 10 degrees, 20 degrees,
45 degrees).
[0206] Processor 210 may be configured to cause selective
conditioning of sounds in the environment of user 100 based on
region 1830. The conditioned audio signal may be transmitted to
hearing interface device 1710, and thus may provide user 100 with
audible feedback corresponding to the look direction of the user.
For example, processor 210 may determine that sound 1820 (which may
correspond to the voice of an individual 1810, or to noise for
example) is within region 1830. Processor 210 may then perform
various conditioning techniques on the audio signals received from
microphone 1720. The conditioning may include amplifying audio
signals determined to correspond to sound 1820 relative to other
audio signals. Amplification may be accomplished digitally, for
example by processing audio signals associated with 1820 relative
to other signals. Amplification may also be accomplished by
changing one or more parameters of microphone 1720 to focus on
audio sounds emanating from region 1830 (e.g., a region of
interest) associated with user look direction 1750. For example,
microphone 1720 may be a directional microphone that and processor
210 may perform an operation to focus microphone 1720 on sound 1820
or other sounds within region 1830. Various other techniques for
amplifying sound 1820 may be used, such as using a beamforming
microphone array, acoustic telescope techniques, etc.
[0207] Conditioning may also include attenuation or suppressing one
or more audio signals received from directions outside of region
1830. For example, processor 1820 may attenuate sounds 1821 and
1822. Similar to amplification of sound 1820, attenuation of sounds
may occur through processing audio signals, or by varying one or
more parameters associated with one or more microphones 1720 to
direct focus away from sounds emanating from outside of region
1830.
[0208] In some embodiments, conditioning may further include
changing a tone of audio signals corresponding to sound 1820 to
make sound 1820 more perceptible to user 100. For example, user 100
may have lesser sensitivity to tones in a certain range and
conditioning of the audio signals may adjust the pitch of sound
1820 to make it more perceptible to user 100. For example, user 100
may experience hearing loss in frequencies above 10 khz.
Accordingly, processor 210 may remap higher frequencies (e.g., at
15 khz) to 10 khz. In some embodiments processor 210 may be
configured to change a rate of speech associated with one or more
audio signals. Accordingly, processor 210 may be configured to
detect speech within one or more audio signals received by
microphone 1720, for example using voice activity detection (VAD)
algorithms or techniques. If sound 1820 is determined to correspond
to voice or speech, for example from individual 1810, processor 220
may be configured to vary the playback rate of sound 1820. For
example, the rate of speech of individual 1810 may be decreased to
make the detected speech more perceptible to user 100. Various
other processing may be performed, such as modifying the tone of
sound 1820 to maintain the same pitch as the original audio signal,
or to reduce noise within the audio signal. If speech recognition
has been performed on the audio signal associated with sound 1820,
conditioning may further include modifying the audio signal based
on the detected speech. For example, processor 210 may introduce
pauses or increase the duration of pauses between words and/or
sentences, which may make the speech easier to understand.
[0209] The conditioned audio signal may then be transmitted to
hearing interface device 1710 and produced for user 100. Thus, in
the conditioned audio signal, sound 1820 may be easier to hear to
user 100, louder and/or more easily distinguishable than sounds
1821 and 1822, which may represent background noise within the
environment.
[0210] FIG. 19 is a flowchart showing an exemplary process 1900 for
selectively amplifying sounds emanating from a detected look
direction of a user consistent with disclosed embodiments. Process
1900 may be performed by one or more processors associated with
apparatus 110, such as processor 210. In some embodiments, some or
all of process 1900 may be performed on processors external to
apparatus 110. In other words, the processor performing process
1900 may be included in a common housing as microphone 1720 and
camera 1730, or may be included in a second housing. For example,
one or more portions of process 1900 may be performed by processors
in hearing interface device 1710, or an auxiliary device, such as
computing device 120.
[0211] In step 1910, process 1900 may include receiving a plurality
of images from an environment of a user captured by a camera. The
camera may be a wearable camera such as camera 1730 of apparatus
110. In step 1912, process 1900 may include receiving audio signals
representative of sounds received by at least one microphone. The
microphone may be configured to capture sounds from an environment
of the user. For example, the microphone may be microphone 1720, as
described above. Accordingly, the microphone may include a
directional microphone, a microphone array, a multi-port
microphone, or various other types of microphones. In some
embodiments, the microphone and wearable camera may be included in
a common housing, such as the housing of apparatus 110. The one or
more processors performing process 1900 may also be included in the
housing or may be included in a second housing. In such
embodiments, the processor(s) may be configured to receive images
and/or audio signals from the common housing via a wireless link
(e.g., Bluetooth.TM., NFC, etc.). Accordingly, the common housing
(e.g., apparatus 110) and the second housing (e.g., computing
device 120) may further comprise transmitters or various other
communication components.
[0212] In step 1914, process 1900 may include determining a look
direction for the user based on analysis of at least one of the
plurality of images. As discussed above, various techniques may be
used to determine the user look direction. In some embodiments, the
look direction may be determined based, at least in part, upon
detection of a representation of a chin of a user in one or more
images. The images may be processed to determine a pointing
direction of the chin relative to an optical axis of the wearable
camera, as discussed above.
[0213] In step 1916, process 1900 may include causing selective
conditioning of at least one audio signal received by the at least
one microphone from a region associated with the look direction of
the user. As described above, the region may be determined based on
the user look direction determined in step 1914. The range may be
associated with an angular width about the look direction (e.g., 10
degrees, 20 degrees, 45 degrees, etc.). Various forms of
conditioning may be performed on the audio signal, as discussed
above. In some embodiments, conditioning may include changing the
tone or playback speed of an audio signal. For example,
conditioning may include changing a rate of speech associated with
the audio signal. In some embodiments, the conditioning may include
amplification of the audio signal relative to other audio signals
received from outside of the region associated with the look
direction of the user. Amplification may be performed by various
means, such as operation of a directional microphone configured to
focus on audio sounds emanating from the region, or varying one or
more parameters associated with the microphone to cause the
microphone to focus on audio sounds emanating from the region. The
amplification may include attenuating or suppressing one or more
audio signals received by the microphone from directions outside
the region associated with the look direction of user 110.
[0214] In step 1918, process 1900 may include causing transmission
of the at least one conditioned audio signal to a hearing interface
device configured to provide sound to an ear of the user. The
conditioned audio signal, for example, may be transmitted to
hearing interface device 1710, which may provide sound
corresponding to the audio signal to user 100. The processor
performing process 1900 may further be configured to cause
transmission to the hearing interface device of one or more audio
signals representative of background noise, which may be attenuated
relative to the at least one conditioned audio signal. For example,
processor 220 may be configured to transmit audio signals
corresponding to sounds 1820, 1821, and 1822. The signal associated
with 1820, however, may be modified in a different manner, for
example amplified, from sounds 1821 and 1822 based on a
determination that sound 1820 is within region 1830. In some
embodiments, hearing interface device 1710 may include a speaker
associated with an earpiece. For example, hearing interface device
may be inserted at least partially into the ear of the user for
providing audio to the user. Hearing interface device may also be
external to the ear, such as a behind-the-ear hearing device, one
or more headphones, a small portable speaker, or the like. In some
embodiments, hearing interface device may include a bone conduction
microphone, configured to provide an audio signal to user through
vibrations of a bone of the user's head. Such devices may be placed
in contact with the exterior of the user's skin, or may be
implanted surgically and attached to the bone of the user.
[0215] Hearing Aid with Voice and/or Image Recognition
[0216] Consistent with the disclosed embodiments, a hearing aid may
selectively amplify audio signals associated with a voice of a
recognized individual. The hearing aid system may store voice
characteristics and/or facial features of a recognized person to
aid in recognition and selective amplification. For example, when
an individual enters the field of view of apparatus 110, the
individual may be recognized as an individual that has been
introduced to the device, or that has possibly interacted with user
100 in the past (e.g., a friend, colleague, relative, prior
acquaintance, etc.). Accordingly, audio signals associated with the
recognized individual's voice may be isolated and/or selectively
amplified relative to other sounds in the environment of the user.
Audio signals associated with sounds received from directions other
than the individual's direction may be suppressed, attenuated,
filtered or the like.
[0217] User 100 may wear a hearing aid device similar to the
camera-based hearing aid device discussed above. For example, the
hearing aid device may be hearing interface device 1720, as shown
in FIG. 17A. Hearing interface device 1710 may be any device
configured to provide audible feedback to user 100. Hearing
interface device 1710 may be placed in one or both ears of user
100, similar to traditional hearing interface devices. As discussed
above, hearing interface device 1710 may be of various styles,
including in-the-canal, completely-in-canal, in-the-ear,
behind-the-ear, on-the-ear, receiver-in-canal, open fit, or various
other styles. Hearing interface device 1710 may include one or more
speakers for providing audible feedback to user 100, a
communication unit for receiving signals from another system, such
as apparatus 110, microphones for detecting sounds in the
environment of user 100, internal electronics, processors,
memories, etc. Hearing interface device 1710 may correspond to
feedback outputting unit 230 or may be separate from feedback
outputting unit 230 and may be configured to receive signals from
feedback outputting unit 230.
[0218] In some embodiments, hearing interface device 1710 may
comprise a bone conduction headphone 1711, as shown in FIG. 17A.
Bone conduction headphone 1711 may be surgically implanted and may
provide audible feedback to user 100 through bone conduction of
sound vibrations to the inner ear. Hearing interface device 1710
may also comprise one or more headphones (e.g., wireless
headphones, over-ear headphones, etc.) or a portable speaker
carried or worn by user 100. In some embodiments, hearing interface
device 1710 may be integrated into other devices, such as a
Bluetooth.TM. headset of the user, glasses, a helmet (e.g.,
motorcycle helmets, bicycle helmets, etc.), a hat, etc.
[0219] Hearing interface device 1710 may be configured to
communicate with a camera device, such as apparatus 110. Such
communication may be through a wired connection, or may be made
wirelessly (e.g., using a Bluetooth.TM., NFC, or forms of wireless
communication). As discussed above, apparatus 110 may be worn by
user 100 in various configurations, including being physically
connected to a shirt, necklace, a belt, glasses, a wrist strap, a
button, or other articles associated with user 100. In some
embodiments, one or more additional devices may also be included,
such as computing device 120. Accordingly, one or more of the
processes or functions described herein with respect to apparatus
110 or processor 210 may be performed by computing device 120
and/or processor 540.
[0220] As discussed above, apparatus 110 may comprise at least one
microphone and at least one image capture device. Apparatus 110 may
comprise microphone 1720, as described with respect to FIG. 17B.
Microphone 1720 may be configured to determine a directionality of
sounds in the environment of user 100. For example, microphone 1720
may comprise one or more directional microphones, a microphone
array, a multi-port microphone, or the like. The microphones shown
in FIG. 17B are by way of example only, and any suitable number,
configuration, or location of microphones may be utilized.
Processor 210 may be configured to distinguish sounds within the
environment of user 100 and determine an approximate directionality
of each sound. For example, using an array of microphones 1720,
processor 210 may compare the relative timing or amplitude of an
individual sound among the microphones 1720 to determine a
directionality relative to apparatus 100. Apparatus 110 may
comprise one or more cameras, such as camera 1730, which may
correspond to image sensor 220. Camera 1730 may be configured to
capture images of the surrounding environment of user 100.
[0221] Apparatus 110 may be configured to recognize an individual
in the environment of user 100. FIG. 20A is a schematic
illustration showing an exemplary environment for use of a hearing
aid with voice and/or image recognition consistent with the present
disclosure. Apparatus 110 may be configured to recognize a face
2011 or voice 2012 associated with an individual 2010 within the
environment of user 100. For example, apparatus 110 may be
configured to capture one or more images of the surrounding
environment of user 100 using camera 1730. The captured images may
include a representation of a recognized individual 2010, which may
be a friend, colleague, relative, or prior acquaintance of user
100. Processor 210 (and/or processors 210a and 210b) may be
configured to analyze the captured images and detect the recognized
user using various facial recognition techniques, as represented by
element 2011. Accordingly, apparatus 110, or specifically memory
550, may comprise one or more facial or voice recognition
components.
[0222] FIG. 20B illustrates an exemplary embodiment of apparatus
110 comprising facial and voice recognition components consistent
with the present disclosure. Apparatus 110 is shown in FIG. 20B in
a simplified form, and apparatus 110 may contain additional
elements or may have alternative configurations, for example, as
shown in FIGS. 5A-5C. Memory 550 (or 550a or 550b) may include
facial recognition component 2040 and voice recognition component
2041. These components may be instead of or in addition to
orientation identification module 601, orientation adjustment
module 602, and motion tracking module 603 as shown in FIG. 6.
Components 2040 and 2041 may contain software instructions for
execution by at least one processing device, e.g., processor 210,
included with a wearable apparatus. Components 2040 and 2041 are
shown within memory 550 by way of example only, and may be located
in other locations within the system. For example, components 2040
and 2041 may be located in hearing interface device 1710, in
computing device 120, on a remote server, or in another associated
device.
[0223] Facial recognition component 2040 may be configured to
identify one or more faces within the environment of user 100. For
example, facial recognition component 2040 may identify facial
features on the face 2011 of individual 2010, such as the eyes,
nose, cheekbones, jaw, or other features. Facial recognition
component 2040 may then analyze the relative size and position of
these features to identify the user. Facial recognition component
2040 may utilize one or more algorithms for analyzing the detected
features, such as principal component analysis (e.g., using
eigenfaces), linear discriminant analysis, elastic bunch graph
matching (e.g., using Fisherface), Local Binary Patterns Histograms
(LBPH), Scale Invariant Feature Transform (SIFT), Speed Up Robust
Features (SURF), or the like. Other facial recognition techniques
such as 3-Dimensional recognition, skin texture analysis, and/or
thermal imaging may also be used to identify individuals. Other
features besides facial features may also be used for
identification, such as the height, body shape, or other
distinguishing features of individual 2010.
[0224] Facial recognition component 2040 may access a database or
data associated with user 100 to determine if the detected facial
features correspond to a recognized individual. For example, a
processor 210 may access a database 2050 containing information
about individuals known to user 100 and data representing
associated facial features or other identifying features. Such data
may include one or more images of the individuals, or data
representative of a face of the user that may be used for
identification through facial recognition. Database 2050 may be any
device capable of storing information about one or more
individuals, and may include a hard drive, a solid state drive, a
web storage platform, a remote server, or the like. Database 2050
may be located within apparatus 110 (e.g., within memory 550) or
external to apparatus 110, as shown in FIG. 20B. In some
embodiments, database 2050 may be associated with a social network
platform, such as Facebook.TM., LinkedIn.TM., Instagram.TM. etc.
Facial recognition component 2040 may also access a contact list of
user 100, such as a contact list on the user's phone, a web-based
contact list (e.g., through Outlook.TM., Skype.TM., Google.TM.,
SalesForce.TM., etc.) or a dedicated contact list associated with
hearing interface device 1710. In some embodiments, database 2050
may be compiled by apparatus 110 through previous facial
recognition analysis. For example, processor 210 may be configured
to store data associated with one or more faces recognized in
images captured by apparatus 110 in database 2050. Each time a face
is detected in the images, the detected facial features or other
data may be compared to previously identified faces in database
2050. Facial recognition component 2040 may determine that an
individual is a recognized individual of user 100 if the individual
has previously been recognized by the system in a number of
instances exceeding a certain threshold, if the individual has been
explicitly introduced to apparatus 110, or the like.
[0225] In some embodiments, user 100 may have access to database
2050, such as through a web interface, an application on a mobile
device, or through apparatus 110 or an associated device. For
example, user 100 may be able to select which contacts are
recognizable by apparatus 110 and/or delete or add certain contacts
manually. In some embodiments, a user or administrator may be able
to train facial recognition component 2040. For example, user 100
may have an option to confirm or reject identifications made by
facial recognition component 2040, which may improve the accuracy
of the system. This training may occur in real time, as individual
2010 is being recognized, or at some later time.
[0226] Other data or information may also inform the facial
identification process. In some embodiments, processor 210 may use
various techniques to recognize the voice of individual 2010, as
described in further detail below. The recognized voice pattern and
the detected facial features may be used, either alone or in
combination, to determine that individual 2010 is recognized by
apparatus 110. Processor 210 may also determine a user look
direction 1750, as described above, which may be used to verify the
identity of individual 2010. For example, if user 100 is looking in
the direction of individual 2010 (especially for a prolonged
period), this may indicate that individual 2010 is recognized by
user 100, which may be used to increase the confidence of facial
recognition component 2040 or other identification means.
[0227] Processor 210 may further be configured to determine whether
individual 2010 is recognized by user 100 based on one or more
detected audio characteristics of sounds associated with a voice of
individual 2010. Returning to FIG. 20A, processor 210 may determine
that sound 2020 corresponds to voice 2012 of user 2010. Processor
210 may analyze audio signals representative of sound 2020 captured
by microphone 1720 to determine whether individual 2010 is
recognized by user 100. This may be performed using voice
recognition component 2041 (FIG. 20B) and may include one or more
voice recognition algorithms, such as Hidden Markov Models, Dynamic
Time Warping, neural networks, or other techniques. Voice
recognition component and/or processor 210 may access database
2050, which may further include a voiceprint of one or more
individuals. Voice recognition component 2041 may analyze the audio
signal representative of sound 2020 to determine whether voice 2012
matches a voiceprint of an individual in database 2050.
Accordingly, database 2050 may contain voiceprint data associated
with a number of individuals, similar to the stored facial
identification data described above. After determining a match,
individual 2010 may be determined to be a recognized individual of
user 100. This process may be used alone, or in conjunction with
the facial recognition techniques described above. For example,
individual 2010 may be recognized using facial recognition
component 2040 and may be verified using voice recognition
component 2041, or vice versa.
[0228] In some embodiments, apparatus 110 may detect the voice of
an individual that is not within the field of view of apparatus
110. For example, the voice may be heard over a speakerphone, from
a back seat, or the like. In such embodiments, recognition of an
individual may be based on the voice of the individual only, in the
absence of a speaker in the field of view. Processor 110 may
analyze the voice of the individual as described above, for
example, by determining whether the detected voice matches a
voiceprint of an individual in database 2050.
[0229] After determining that individual 2010 is a recognized
individual of user 100, processor 210 may cause selective
conditioning of audio associated with the recognized individual.
The conditioned audio signal may be transmitted to hearing
interface device 1710, and thus may provide user 100 with audio
conditioned based on the recognized individual. For example, the
conditioning may include amplifying audio signals determined to
correspond to sound 2020 (which may correspond to voice 2012 of
individual 2010) relative to other audio signals. In some
embodiments, amplification may be accomplished digitally, for
example by processing audio signals associated with sound 2020
relative to other signals. Additionally, or alternatively,
amplification may be accomplished by changing one or more
parameters of microphone 1720 to focus on audio sounds associated
with individual 2010. For example, microphone 1720 may be a
directional microphone and processor 210 may perform an operation
to focus microphone 1720 on sound 2020. Various other techniques
for amplifying sound 2020 may be used, such as using a beamforming
microphone array, acoustic telescope techniques, etc.
[0230] In some embodiments, selective conditioning may include
attenuation or suppressing one or more audio signals received from
directions not associated with individual 2010. For example,
processor 210 may attenuate sounds 2021 and/or 2022. Similar to
amplification of sound 2020, attenuation of sounds may occur
through processing audio signals, or by varying one or more
parameters associated with microphone 1720 to direct focus away
from sounds not associated with individual 2010.
[0231] Selective conditioning may further include determining
whether individual 2010 is speaking. For example, processor 210 may
be configured to analyze images or videos containing
representations of individual 2010 to determine when individual
2010 is speaking, for example, based on detected movement of the
recognized individual's lips. This may also be determined through
analysis of audio signals received by microphone 1720, for example
by detecting the voice 2012 of individual 2010. In some
embodiments, the selective conditioning may occur dynamically
(initiated and/or terminated) based on whether or not the
recognized individual is speaking.
[0232] In some embodiments, conditioning may further include
changing a tone of one or more audio signals corresponding to sound
2020 to make the sound more perceptible to user 100. For example,
user 100 may have lesser sensitivity to tones in a certain range
and conditioning of the audio signals may adjust the pitch of sound
2020. In some embodiments processor 210 may be configured to change
a rate of speech associated with one or more audio signals. For
example, sound 2020 may be determined to correspond to voice 2012
of individual 2010. Processor 210 may be configured to vary the
rate of speech of individual 2010 to make the detected speech more
perceptible to user 100. Various other processing may be performed,
such as modifying the tone of sound 2020 to maintain the same pitch
as the original audio signal, or to reduce noise within the audio
signal.
[0233] In some embodiments, processor 210 may determine a region
2030 associated with individual 2010. Region 2030 may be associated
with a direction of individual 2010 relative to apparatus 110 or
user 100. The direction of individual 2010 may be determined using
camera 1730 and/or microphone 1720 using the methods described
above. As shown in FIG. 20A, region 2030 may be defined by a cone
or range of directions based on a determined direction of
individual 2010. The range of angles may be defined by an angle,
.theta., as shown in FIG. 20A. The angle, .theta., may be any
suitable angle for defining a range for conditioning sounds within
the environment of user 100 (e.g., 10 degrees, 20 degrees, 45
degrees). Region 2030 may be dynamically calculated as the position
of individual 2010 changes relative to apparatus 110. For example,
as user 100 turns, or if individual 1020 moves within the
environment, processor 210 may be configured to track individual
2010 within the environment and dynamically update region 2030.
Region 2030 may be used for selective conditioning, for example by
amplifying sounds associated with region 2030 and/or attenuating
sounds determined to be emanating from outside of region 2030.
[0234] The conditioned audio signal may then be transmitted to
hearing interface device 1710 and produced for user 100. Thus, in
the conditioned audio signal, sound 2020 (and specifically voice
2012) may be louder and/or more easily distinguishable than sounds
2021 and 2022, which may represent background noise within the
environment.
[0235] In some embodiments, processor 210 may perform further
analysis based on captured images or videos to determine how to
selectively condition audio signals associated with a recognized
individual. In some embodiments, processor 210 may analyze the
captured images to selectively condition audio associated with one
individual relative to others. For example, processor 210 may
determine the direction of a recognized individual relative to the
user based on the images and may determine how to selectively
condition audio signals associated with the individual based on the
direction. If the recognized individual is standing to the front of
the user, audio associated with that user may be amplified (or
otherwise selectively conditioned) relative to audio associated
with an individual standing to the side of the user. Similarly,
processor 210 may selectively condition audio signals associated
with an individual based on proximity to the user. Processor 210
may determine a distance from the user to each individual based on
captured images and may selectively condition audio signals
associated with the individuals based on the distance. For example,
an individual closer to the user may be prioritized higher than an
individual that is farther away.
[0236] In some embodiments, selective conditioning of audio signals
associated with a recognized individual may be based on the
identities of individuals within the environment of the user. For
example, where multiple individuals are detected in the images,
processor 210 may use one or more facial recognition techniques to
identify the individuals, as described above. Audio signals
associated with individuals that are known to user 100 may be
selectively amplified or otherwise conditioned to have priority
over unknown individuals. For example, processor 210 may be
configured to attenuate or silence audio signals associated with
bystanders in the user's environment, such as a noisy office mate,
etc. In some embodiments, processor 210 may also determine a
hierarchy of individuals and give priority based on the relative
status of the individuals. This hierarchy may be based on the
individual's position within a family or an organization (e.g., a
company, sports team, club, etc.) relative to the user. For
example, the user's boss may be ranked higher than a co-worker or a
member of the maintenance staff and thus may have priority in the
selective conditioning process. In some embodiments, the hierarchy
may be determined based on a list or database. Individuals
recognized by the system may be ranked individually or grouped into
tiers of priority. This database may be maintained specifically for
this purpose, or may be accessed externally. For example, the
database may be associated with a social network of the user (e.g.,
Facebook.TM., LinkedIn.TM., etc.) and individuals may be
prioritized based on their grouping or relationship with the user.
Individuals identified as "close friends" or family, for example,
may be prioritized over acquaintances of the user.
[0237] Selective conditioning may be based on a determined behavior
of one or more individuals determined based on the captured images.
In some embodiments, processor 210 may be configured to determine a
look direction of the individuals in the images. Accordingly, the
selective conditioning may be based on behavior of the other
individuals towards the recognized individual. For example,
processor 210 may selectively condition audio associated with a
first individual that one or more other users are looking at. If
the attention of the individuals shifts to a second individual,
processor 210 may then switch to selectively condition audio
associated with the second user. In some embodiments, processor 210
may be configured to selectively condition audio based on whether a
recognized individual is speaking to the user or to another
individual. For example, when the recognized individual is speaking
to the user, the selective conditioning may include amplifying an
audio signal associated with the recognized individual relative to
other audio signals received from directions outside a region
associated with the recognized individual. When the recognized
individual is speaking to another individual, the selective
conditioning may include attenuating the audio signal relative to
other audio signals received from directions outside the region
associated with the recognized individual.
[0238] In some embodiments, processor 210 may have access to one or
more voiceprints of individuals, which may facilitate selective
conditioning of voice 2012 of individual 2010 in relation to other
sounds or voices. Having a speaker's voiceprint, and a high quality
voiceprint in particular, may provide for fast and efficient
speaker separation. A high quality voice print may be collected,
for example, when the user speaks alone, preferably in a quiet
environment. By having a voiceprint of one or more speakers, it is
possible to separate an ongoing voice signal almost in real time,
e.g. with a minimal delay, using a sliding time window. The delay
may be, for example 10 ms, 20 ms, 30 ms, 50 ms, 100 ms, or the
like. Different time windows may be selected, depending on the
quality of the voice print, on the quality of the captured audio,
the difference in characteristics between the speaker and other
speaker(s), the available processing resources, the required
separation quality, or the like. In some embodiments, a voice print
may be extracted from a segment of a conversation in which an
individual speaks alone, and then used for separating the
individual's voice later in the conversation, whether the
individual's is recognized or not.
[0239] Separating voices may be performed as follows: spectral
features, also referred to as spectral attributes, spectral
envelope, or spectrogram may be extracted from a clean audio of a
single speaker and fed into a pre-trained first neural network,
which generates or updates a signature of the speaker's voice based
on the extracted features. The audio may be for example, of one
second of clean voice. The output signature may be a vector
representing the speaker's voice, such that the distance between
the vector and another vector extracted from the voice of the same
speaker is typically smaller than the distance between the vector
and a vector extracted from the voice of another speaker. The
speaker's model may be pre-generated from a captured audio.
Alternatively or additionally, the model may be generated after a
segment of the audio in which only the speaker speaks, followed by
another segment in which the speaker and another speaker (or
background noise) is heard, and which it is required to
separate.
[0240] Then, to separate the speaker's voice from additional
speakers or background noise in a noisy audio, a second pre-trained
neural network may receive the noisy audio and the speaker's
signature, and output an audio (which may also be represented as
attributes) of the voice of the speaker as extracted from the noisy
audio, separated from the other speech or background noise. It will
be appreciated that the same or additional neural networks may be
used to separate the voices of multiple speakers. For example, if
there are two possible speakers, two neural networks may be
activated, each with models of the same noisy output and one of the
two speakers. Alternatively, a neural network may receive voice
signatures of two or more speakers, and output the voice of each of
the speakers separately. Accordingly, the system may generate two
or more different audio outputs, each comprising the speech of the
respective speaker. In some embodiments, if separation is
impossible, the input voice may only be cleaned from background
noise.
[0241] FIG. 21 is a flowchart showing an exemplary process 2100 for
selectively amplifying audio signals associated with a voice of a
recognized individual consistent with disclosed embodiments.
Process 2100 may be performed by one or more processors associated
with apparatus 110, such as processor 210. In some embodiments,
some or all of process 2100 may be performed on processors external
to apparatus 110. In other words, the processor performing process
2100 may be included in the same common housing as microphone 1720
and camera 1730, or may be included in a second housing. For
example, one or more portions of process 2100 may be performed by
processors in hearing interface device 1710, or in an auxiliary
device, such as computing device 120.
[0242] In step 2110, process 2100 may include receiving a plurality
of images from an environment of a user captured by a camera. The
images may be captured by a wearable camera such as camera 1730 of
apparatus 110. In step 2112, process 2100 may include identifying a
representation of a recognized individual in at least one of the
plurality of images Individual 2010 may be recognized by processor
210 using facial recognition component 2040, as described above.
For example, individual 2010 may be a friend, colleague, relative,
or prior acquaintance of the user. Processor 210 may determine
whether an individual represented in at least one of the plurality
of images is a recognized individual based on one or more detected
facial features associated with the individual. Processor 210 may
also determine whether the individual is recognized based on one or
more detected audio characteristics of sounds determined to be
associated with a voice of the individual, as described above.
[0243] In step 2114, process 2100 may include receiving audio
signals representative of sounds captured by a microphone. For
example, apparatus 110 may receive audio signals representative of
sounds 2020, 2021, and 2022, captured by microphone 1720.
Accordingly, the microphone may include a directional microphone, a
microphone array, a multi-port microphone, or various other types
of microphones, as described above. In some embodiments, the
microphone and wearable camera may be included in a common housing,
such as the housing of apparatus 110. The one or more processors
performing process 2100 may also be included in the housing (e.g.,
processor 210), or may be included in a second housing. Where a
second housing is used, the processor(s) may be configured to
receive images and/or audio signals from the common housing via a
wireless link (e g, Bluetooth.TM., NFC, etc.). Accordingly, the
common housing (e.g., apparatus 110) and the second housing (e.g.,
computing device 120) may further comprise transmitters, receivers,
and/or various other communication components.
[0244] In step 2116, process 2100 may include cause selective
conditioning of at least one audio signal received by the at least
one microphone from a region associated with the at least one
recognized individual. As described above, the region may be
determined based on a determined direction of the recognized
individual based one or more of the plurality of images or audio
signals. The range may be associated with an angular width about
the direction of the recognized individual (e.g., 10 degrees, 20
degrees, 45 degrees, etc.).
[0245] Various forms of conditioning may be performed on the audio
signal, as discussed above. In some embodiments, conditioning may
include changing the tone or playback speed of an audio signal. For
example, conditioning may include changing a rate of speech
associated with the audio signal. In some embodiments, the
conditioning may include amplification of the audio signal relative
to other audio signals received from outside of the region
associated with the recognized individual. Amplification may be
performed by various means, such as operation of a directional
microphone configured to focus on audio sounds emanating from the
region or varying one or more parameters associated with the
microphone to cause the microphone to focus on audio sounds
emanating from the region. The amplification may include
attenuating or suppressing one or more audio signals received by
the microphone from directions outside the region. In some
embodiments, step 2116 may further comprise determining, based on
analysis of the plurality of images, that the recognized individual
is speaking and trigger the selective conditioning based on the
determination that the recognized individual is speaking. For
example, the determination that the recognized individual is
speaking may be based on detected movement of the recognized
individual's lips. In some embodiments, selective conditioning may
be based on further analysis of the captured images as described
above, for example, based on the direction or proximity of the
recognized individual, the identity of the recognized individual,
the behavior of other individuals, etc.
[0246] In step 2118, process 2100 may include causing transmission
of the at least one conditioned audio signal to a hearing interface
device configured to provide sound to an ear of the user. The
conditioned audio signal, for example, may be transmitted to
hearing interface device 1710, which may provide sound
corresponding to the audio signal to user 100. The processor
performing process 2100 may further be configured to cause
transmission to the hearing interface device of one or more audio
signals representative of background noise, which may be attenuated
relative to the at least one conditioned audio signal. For example,
processor 210 may be configured to transmit audio signals
corresponding to sounds 2020, 2021, and 2022. The signal associated
with 2020, however, may be amplified in relation to sounds 2021 and
2022 based on a determination that sound 2020 is within region
2030. In some embodiments, hearing interface device 1710 may
include a speaker associated with an earpiece. For example, hearing
interface device 1710 may be inserted at least partially into the
ear of the user for providing audio to the user. Hearing interface
device may also be external to the ear, such as a behind-the-ear
hearing device, one or more headphones, a small portable speaker,
or the like. In some embodiments, hearing interface device may
include a bone conduction microphone, configured to provide an
audio signal to user through vibrations of a bone of the user's
head. Such devices may be placed in contact with the exterior of
the user's skin, or may be implanted surgically and attached to the
bone of the user.
[0247] In addition to recognizing voices of individuals speaking to
user 100, the systems and methods described above may also be used
to recognize the voice of user 100. For example, voice recognition
unit 2041 may be configured to analyze audio signals representative
of sounds collected from the user's environment to recognize the
voice of user 100. Similar to the selective conditioning of the
voice of recognized individuals, the voice of user 100 may be
selectively conditioned. For example, sounds may be collected by
microphone 1720, or by a microphone of another device, such as a
mobile phone (or a device linked to a mobile phone). Audio signals
corresponding to the voice of user 100 may be selectively
transmitted to a remote device, for example, by amplifying the
voice of user 100 and/or attenuating or eliminating altogether
sounds other than the user's voice. Accordingly, a voiceprint of
one or more users of apparatus 110 may be collected and/or stored
to facilitate detection and/or isolation of the user's voice, as
described in further detail above.
[0248] FIG. 22 is a flowchart showing an exemplary process 2200 for
selectively transmitting audio signals associated with a voice of a
recognized user consistent with disclosed embodiments. Process 2200
may be performed by one or more processors associated with
apparatus 110, such as processor 210.
[0249] In step 2210, process 2200 may include receiving audio
signals representative of sounds captured by a microphone. For
example, apparatus 110 may receive audio signals representative of
sounds 2020, 2021, and 2022, captured by microphone 1720.
Accordingly, the microphone may include a directional microphone, a
microphone array, a multi-port microphone, or various other types
of microphones, as described above. In step 2212, process 2200 may
include identifying, based on analysis of the received audio
signals, one or more voice audio signals representative of a
recognized voice of the user. For example, the voice of the user
may be recognized based on a voiceprint associated with the user,
which may be stored in memory 550, database 2050, or other suitable
locations. Processor 210 may recognize the voice of the user, for
example, using voice recognition component 2041. Processor 210 may
separate an ongoing voice signal associated with the user almost in
real time, e.g. with a minimal delay, using a sliding time window.
The voice may be separated by extracting spectral features of an
audio signal according to the methods described above.
[0250] In step 2214, process 2200 may include causing transmission,
to a remotely located device, of the one or more voice audio
signals representative of the recognized voice of the user. The
remotely located device may be any device configured to receive
audio signals remotely, either by a wired or wireless form of
communication. In some embodiments, the remotely located device may
be another device of the user, such as a mobile phone, an audio
interface device, or another form of computing device. In some
embodiments, the voice audio signals may be processed by the
remotely located device and/or transmitted further. In step 2216,
process 2200 may include preventing transmission, to the remotely
located device, of at least one background noise audio signal
different from the one or more voice audio signals representative
of a recognized voice of the user. For example, processor 210 may
attenuate and/or eliminate audio signals associated with sounds
2020, 2021, or 2023, which may represent background noise. The
voice of the user may be separated from other noises using the
audio processing techniques described above.
[0251] In an exemplary illustration, the voice audio signals may be
captured by a headset or other device worn by the user. The voice
of the user may be recognized and isolated from the background
noise in the environment of the user. The headset may transmit the
conditioned audio signal of the user's voice to a mobile phone of
the user. For example, the user may be on a telephone call and the
conditioned audio signal may be transmitted by the mobile phone to
a recipient of the call. The voice of the user may also be recorded
by the remotely located device. The audio signal, for example, may
be stored on a remote server or other computing device. In some
embodiments, the remotely located device may process the received
audio signal, for example, to convert the recognized user's voice
into text.
[0252] Lip-Tracking Hearing Aid
[0253] Consistent with the disclosed embodiments, a hearing aid
system may selectively amplify audio signals based on tracked lip
movements. The hearing aid system analyzes captured images of the
environment of a user to detect lips of an individual and track
movement of the individual's lips. The tracked lip movements may
serve as a cue for selectively amplifying audio received by the
hearing aid system. For example, voice signals determined to sync
with the tracked lip movements or that are consistent with the
tracked lip movements may be selectively amplified or otherwise
conditioned. Audio signals that are not associated with the
detected lip movement may be suppressed, attenuated, filtered or
the like.
[0254] User 100 may wear a hearing aid device consistent with the
camera-based hearing aid device discussed above. For example, the
hearing aid device may be hearing interface device 1710, as shown
in FIG. 17A. Hearing interface device 1710 may be any device
configured to provide audible feedback to user 100. Hearing
interface device 1710 may be placed in one or both ears of user
100, similar to traditional hearing interface devices. As discussed
above, hearing interface device 1710 may be of various styles,
including in-the-canal, completely-in-canal, in-the-ear,
behind-the-ear, on-the-ear, receiver-in-canal, open fit, or various
other styles. Hearing interface device 1710 may include one or more
speakers for providing audible feedback to user 100, microphones
for detecting sounds in the environment of user 100, internal
electronics, processors, memories, etc. In some embodiments, in
addition to or instead of a microphone, hearing interface device
1710 may comprise one or more communication units, and one or more
receivers for receiving signals from apparatus 110 and transferring
the signals to user 100. Hearing interface device 1710 may
correspond to feedback outputting unit 230 or may be separate from
feedback outputting unit 230 and may be configured to receive
signals from feedback outputting unit 230.
[0255] In some embodiments, hearing interface device 1710 may
comprise a bone conduction headphone 1711, as shown in FIG. 17A.
Bone conduction headphone 1711 may be surgically implanted and may
provide audible feedback to user 100 through bone conduction of
sound vibrations to the inner ear. Hearing interface device 1710
may also comprise one or more headphones (e.g., wireless
headphones, over-ear headphones, etc.) or a portable speaker
carried or worn by user 100. In some embodiments, hearing interface
device 1710 may be integrated into other devices, such as a
Bluetooth.TM. headset of the user, glasses, a helmet (e.g.,
motorcycle helmets, bicycle helmets, etc.), a hat, etc.
[0256] Hearing interface device 1710 may be configured to
communicate with a camera device, such as apparatus 110. Such
communication may be through a wired connection, or may be made
wirelessly (e.g., using a Bluetooth.TM., NFC, or forms of wireless
communication). As discussed above, apparatus 110 may be worn by
user 100 in various configurations, including being physically
connected to a shirt, necklace, a belt, glasses, a wrist strap, a
button, or other articles associated with user 100. In some
embodiments, one or more additional devices may also be included,
such as computing device 120. Accordingly, one or more of the
processes or functions described herein with respect to apparatus
110 or processor 210 may be performed by computing device 120
and/or processor 540.
[0257] As discussed above, apparatus 110 may comprise at least one
microphone and at least one image capture device. Apparatus 110 may
comprise microphone 1720, as described with respect to FIG. 17B.
Microphone 1720 may be configured to determine a directionality of
sounds in the environment of user 100. For example, microphone 1720
may comprise one or more directional microphones, a microphone
array, a multi-port microphone, or the like. Processor 210 may be
configured to distinguish sounds within the environment of user 100
and determine an approximate directionality of each sound. For
example, using an array of microphones 1720, processor 210 may
compare the relative timing or amplitude of an individual sound
among the microphones 1720 to determine a directionality relative
to apparatus 100. Apparatus 110 may comprise one or more cameras,
such as camera 1730, which may correspond to image sensor 220.
Camera 1730 may be configured to capture images of the surrounding
environment of user 100. Apparatus 110 may also use one or more
microphones of hearing interface device 1710 and, accordingly,
references to microphone 1720 used herein may also refer to a
microphone on hearing interface device 1710.
[0258] Processor 210 (and/or processors 210a and 210b) may be
configured to detect a mouth and/or lips associated with an
individual within the environment of user 100. FIGS. 23A and 23B
show an exemplary individual 2310 that may be captured by camera
1730 in the environment of a user consistent with the present
disclosure. As shown in FIG. 23, individual 2310 may be physically
present with the environment of user 100. Processor 210 may be
configured to analyze images captured by camera 1730 to detect a
representation of individual 2310 in the images. Processor 210 may
use a facial recognition component, such as facial recognition
component 2040, described above, to detect and identify individuals
in the environment of user 100. Processor 210 may be configured to
detect one or more facial features of user 2310, including a mouth
2311 of individual 2310. Accordingly, processor 210 may use one or
more facial recognition and/or feature recognition techniques, as
described further below.
[0259] In some embodiments, processor 210 may detect a visual
representation of individual 2310 from the environment of user 100,
such as a video of user 2310. As shown in FIG. 23B, user 2310 may
be detected on the display of a display device 2301. Display device
2301 may be any device capable of displaying a visual
representation of an individual. For example, display device may be
a personal computer, a laptop, a mobile phone, a tablet, a
television, a movie screen, a handheld gaming device, a video
conferencing device (e.g., Facebook Portal.TM., etc.), a baby
monitor, etc. The visual representation of individual 2310 may be a
live video feed of individual 2310, such as a video call, a
conference call, a surveillance video, etc. In other embodiments,
the visual representation of individual 2310 may be a prerecorded
video or image, such as a video message, a television program, or a
movie. Processor 210 may detect one or more facial features based
on the visual representation of individual 2310, including a mouth
2311 of individual 2310.
[0260] FIG. 23C illustrates an exemplary lip-tracking system
consistent with the disclosed embodiments. Processor 210 may be
configured to detect one or more facial features of individual
2310, which may include, but is not limited to the individual's
mouth 2311. Accordingly, processor 210 may use one or more image
processing techniques to recognize facial features of the user,
such as convolutional neural networks (CNN), scale-invariant
feature transform (SIFT), histogram of oriented gradients (HOG)
features, or other techniques. In some embodiments, processor 210
may be configured to detect one or more points 2320 associated with
the mouth 2311 of individual 2310. Points 2320 may represent one or
more characteristic points of an individual's mouth, such as one or
more points along the individual's lips or the corner of the
individual's mouth. The points shown in FIG. 23C are for
illustrative purposes only and it is understood that any points for
tracking the individual's lips may be determined or identified via
one or more image processing techniques. Points 2320 may be
detected at various other locations, including points associated
with the individual's teeth, tongue, cheek, chin, eyes, etc.
Processor 210 may determine one or more contours of mouth 2311
(e.g., represented by lines or polygons) based on points 2320 or
based on the captured image. The contour may represent the entire
mouth 2311 or may comprise multiple contours, for example including
a contour representing an upper lip and a contour representing a
lower lip. Each lip may also be represented by multiple contours,
such as a contour for the upper edge and a contour for the lower
edge of each lip. Processor 210 may further use various other
techniques or characteristics, such as color, edge, shape or motion
detection algorithms to identify the lips of individual 2310. The
identified lips may be tracked over multiple frames or images.
Processor 210 may use one or more video tracking algorithms, such
as mean-shift tracking, contour tracking (e.g., a condensation
algorithm), or various other techniques. Accordingly, processor 210
may be configured to track movement of the lips of individual 2310
in real time.
[0261] The tracked lip movement of individual 2310 may be used to
separate if required, and selectively condition one or more sounds
in the environment of user 100. FIG. 24 is a schematic illustration
showing an exemplary environment 2400 for use of a lip-tracking
hearing aid consistent with the present disclosure. Apparatus 110,
worn by user 100 may be configured to identify one or more
individuals within environment 2400. For example, apparatus 110 may
be configured to capture one or more images of the surrounding
environment 2400 using camera 1730. The captured images may include
a representation of individuals 2310 and 2410, who may be present
in environment 2400. Processor 210 may be configured to detect a
mouth of individuals 2310 and 2410 and track their respective lip
movements using the methods described above. In some embodiments,
processor 210 may further be configured to identify individuals
2310 and 2410, for example, by detecting facial features of
individuals 2310 and 2410 and comparing them to a database, as
discussed previously.
[0262] In addition to detecting images, apparatus 110 may be
configured to detect one or more sounds in the environment of user
100. For example, microphone 1720 may detect one or more sounds
2421, 2422, and 2423 within environment 2400. In some embodiments,
the sounds may represent voices of various individuals. For
example, as shown in FIG. 24, sound 2421 may represent a voice of
individual 2310 and sound 2422 may represent a voice of individual
2410. Sound 2423 may represent additional voices and/or background
noise within environment 2400. Processor 210 may be configured to
analyze sounds 2421, 2422, and 2423 to separate and identify audio
signals associated with voices. For example, processor 210 may use
one or more speech or voice activity detection (VAD) algorithms
and/or the voice separation techniques described above. When there
are multiple voices detected in the environment, processor 210 may
isolate audio signals associated with each voice. In some
embodiments, processor 210 may perform further analysis on the
audio signal associated the detected voice activity to recognize
the speech of the individual. For example, processor 210 may use
one or more voice recognition algorithms (e.g., Hidden Markov
Models, Dynamic Time Warping, neural networks, or other techniques)
to recognize the voice of the individual. Processor 210 may also be
configured to recognize the words spoken by individual 2310 using
various speech-to-text algorithms. In some embodiments, instead of
using microphone 1710, apparatus 110 may receive audio signals from
another device through a communication component, such as wireless
transceiver 530. For example, if user 100 is on a video call,
apparatus 110 may receive an audio signal representing a voice of
user 2310 from display device 2301 or another auxiliary device.
[0263] Processor 210 may determine, based on lip movements and the
detected sounds, which individuals in environment 2400 are
speaking. For example, processor 2310 may track lip movements
associated with mouth 2311 to determine that individual 2310 is
speaking. A comparative analysis may be performed between the
detected lip movement and the received audio signals. In some
embodiments, processor 210 may determine that individual 2310 is
speaking based on a determination that mouth 2311 is moving at the
same time as sound 2421 is detected. For example, when the lips of
individual 2310 stop moving, this may correspond with a period of
silence or reduced volume in the audio signal associated with sound
2421. In some embodiments, processor 210 may be configured to
determine whether specific movements of mouth 2311 correspond to
the received audio signal. For example, processor 210 may analyze
the received audio signal to identify specific phonemes, phoneme
combinations or words in the received audio signal. Processor 210
may recognize whether specific lip movements of mouth 2311
correspond to the identified words or phonemes. Various machine
learning or deep learning techniques may be implemented to
correlate the expected lip movements to the detected audio. For
example, a training data set of known sounds and corresponding lip
movements may be fed to a machine learning algorithm to develop a
model for correlating detected sounds with expected lip movements.
Other data associated with apparatus 110 may further be used in
conjunction with the detected lip movement to determine and/or
verify whether individual 2310 is speaking, such as a look
direction of user 100 or individual 2310, a detected identity of
user 2310, a recognized voiceprint of user 2310, etc.
[0264] Based on the detected lip movement, processor 210 may cause
selective conditioning of audio associated with individual 2310.
The conditioning may include amplifying audio signals determined to
correspond to sound 2421 (which may correspond to a voice of
individual 2310) relative to other audio signals. In some
embodiments, amplification may be accomplished digitally, for
example by processing audio signals associated with sound 2421
relative to other signals. Additionally, or alternatively,
amplification may be accomplished by changing one or more
parameters of microphone 1720 to focus on audio sounds associated
with individual 2310. For example, microphone 1720 may be a
directional microphone and processor 210 may perform an operation
to focus microphone 1720 on sound 2421. Various other techniques
for amplifying sound 2421 may be used, such as using a beamforming
microphone array, acoustic telescope techniques, etc. The
conditioned audio signal may be transmitted to hearing interface
device 1710, and thus may provide user 100 with audio conditioned
based on the individual who is speaking.
[0265] In some embodiments, selective conditioning may include
attenuation or suppressing one or more audio signals not associated
with individual 2310, such as sounds 2422 and 2423. Similar to
amplification of sound 2421, attenuation of sounds may occur
through processing audio signals, or by varying one or more
parameters associated with microphone 1720 to direct focus away
from sounds not associated with individual 2310.
[0266] In some embodiments, conditioning may further include
changing a tone of one or more audio signals corresponding to sound
2421 to make the sound more perceptible to user 100. For example,
user 100 may have lesser sensitivity to tones in a certain range
and conditioning of the audio signals may adjust the pitch of sound
2421. For example, user 100 may experience hearing loss in
frequencies above 10 kHz and processor 210 may remap higher
frequencies (e.g., at 15 kHz) to 10 kHz. In some embodiments
processor 210 may be configured to change a rate of speech
associated with one or more audio signals. Processor 210 may be
configured to vary the rate of speech of individual 2310 to make
the detected speech more perceptible to user 100. If speech
recognition has been performed on the audio signal associated with
sound 2421, conditioning may further include modifying the audio
signal based on the detected speech. For example, processor 210 may
introduce pauses or increase the duration of pauses between words
and/or sentences, which may make the speech easier to understand.
Various other processing may be performed, such as modifying the
tone of sound 2421 to maintain the same pitch as the original audio
signal, or to reduce noise within the audio signal.
[0267] The conditioned audio signal may then be transmitted to
hearing interface device 1710 and then produced for user 100. Thus,
in the conditioned audio signal, sound 2421 (may be louder and/or
more easily distinguishable than sounds 2422 and 2423.
[0268] Processor 210 may be configured to selectively condition
multiple audio signals based on which individuals associated with
the audio signals are currently speaking. For example, individual
2310 and individual 2410 may be engaged in a conversation within
environment 2400 and processor 210 may be configured to transition
from conditioning of audio signals associated with sound 2421 to
conditioning of audio signals associated with sound 2422 based on
the respective lip movements of individuals 2310 and 2410. For
example, lip movements of individual 2310 may indicate that
individual 2310 has stopped speaking or lip movements associated
with individual 2410 may indicate that individual 2410 has started
speaking. Accordingly, processor 210 may transition between
selectively conditioning audio signals associated with sound 2421
to audio signals associated with sound 2422. In some embodiments,
processor 210 may be configured to process and/or condition both
audio signals concurrently but only selectively transmit the
conditioned audio to hearing interface device 1710 based on which
individual is speaking. Where speech recognition is implemented,
processor 210 may determine and/or anticipate a transition between
speakers based on the context of the speech. For example, processor
210 may analyze audio signals associate with sound 2421 to
determine that individual 2310 has reached the end of a sentence or
has asked a question, which may indicate individual 2310 has
finished or is about to finish speaking.
[0269] In some embodiments, processor 210 may be configured to
select between multiple active speakers to selectively condition
audio signals. For example, individuals 2310 and 2410 may both be
speaking at the same time or their speech may overlap during a
conversation. Processor 210 may selectively condition audio
associated with one speaking individual relative to others. This
may include giving priority to a speaker who has started but not
finished a word or sentence or has not finished speaking altogether
when the other speaker started speaking. This determination may
also be driven by the context of the speech, as described
above.
[0270] Various other factors may also be considered in selecting
among active speakers. For example, a look direction of the user
may be determined and the individual in the look direction of the
user may be given higher priority among the active speakers.
Priority may also be assigned based on the look direction of the
speakers. For example, if individual 2310 is looking at user 100
and individual 2410 is looking elsewhere, audio signals associated
with individual 2310 may be selectively conditioned. In some
embodiments, priority may be assigned based on the relative
behavior of other individuals in environment 2400. For example, if
both individual 2310 and individual 2410 are speaking and more
other individuals are looking at individual 2410 than individual
2310, audio signals associated with individual 2410 may be
selectively conditioned over those associated with individual 2310.
In embodiments where the identity of the individuals is determined,
priority may be assigned based on the relative status of the
speakers, as discussed previously in greater detail. User 100 may
also provide input into which speakers are prioritized through
predefined settings or by actively selecting which speaker to focus
on.
[0271] Processor 210 may also assign priority based on how the
representation of individual 2310 is detected. While individuals
2310 and 2410 are shown to be physically present in environment
2400, one or more individuals may be detected as visual
representations of the individual (e.g., on a display device) as
shown in FIG. 23B. Processor 210 may prioritize speakers based on
whether or not they are physically present in environment 2400. For
example, processor 210 may prioritize speakers who are physically
present over speakers on a display. Alternatively, processor 210
may prioritize a video over speakers in a room, for example, if
user 100 is on a video conference or if user 100 is watching a
movie. The prioritized speaker or speaker type (e.g. present or
not) may also be indicated by user 100, using a user interface
associated with apparatus 110.
[0272] FIG. 25 is a flowchart showing an exemplary process 2500 for
selectively amplifying audio signals based on tracked lip movements
consistent with disclosed embodiments. Process 2500 may be
performed by one or more processors associated with apparatus 110,
such as processor 210. The processor(s) may be included in the same
common housing as microphone 1720 and camera 1730, which may also
be used for process 2500. In some embodiments, some or all of
process 2500 may be performed on processors external to apparatus
110, which may be included in a second housing. For example, one or
more portions of process 2500 may be performed by processors in
hearing interface device 1710, or in an auxiliary device, such as
computing device 120 or display device 2301. In such embodiments,
the processor may be configured to receive the captured images via
a wireless link between a transmitter in the common housing and
receiver in the second housing.
[0273] In step 2510, process 2500 may include receiving a plurality
of images captured by a wearable camera from an environment of the
user. The images may be captured by a wearable camera such as
camera 1730 of apparatus 110. In step 2520, process 2500 may
include identifying a representation of at least one individual in
at least one of the plurality of images. The individual may be
identified using various image detection algorithms, such as Haar
cascade, histograms of oriented gradients (HOG), deep convolution
neural networks (CNN), scale-invariant feature transform (SIFT), or
the like. In some embodiments, processor 210 may be configured to
detect visual representations of individuals, for example from a
display device, as shown in FIG. 23B.
[0274] In step 2530, process 2500 may include identifying at least
one lip movement or lip position associated with a mouth of the
individual, based on analysis of the plurality of images. Processor
210 may be configured to identify one or more points associated
with the mouth of the individual. In some embodiments, processor
210 may develop a contour associated with the mouth of the
individual, which may define a boundary associated with the mouth
or lips of the individual. The lips identified in the image may be
tracked over multiple frames or images to identify the lip
movement. Accordingly, processor 210 may use various video tracking
algorithms, as described above.
[0275] In step 2540, process 2500 may include receiving audio
signals representative of the sounds captured by a microphone from
the environment of the user. For example, apparatus 110 may receive
audio signals representative of sounds 2421, 2422, and 2423
captured by microphone 1720. In step 2550, process 2500 may include
identifying, based on analysis of the sounds captured by the
microphone, a first audio signal associated with a first voice and
a second audio signal associated with a second voice different from
the first voice. For example, processor 210 may identify an audio
signal associated with sounds 2421 and 2422, representing the voice
of individuals 2310 and 2410, respectively. Processor 210 may
analyze the sounds received from microphone 1720 to separate the
first and second voices using any currently known or future
developed techniques or algorithms. Step 2550 may also include
identifying additional sounds, such as sound 2423 which may include
additional voices or background noise in the environment of the
user. In some embodiments, processor 210 may perform further
analysis on the first and second audio signals, for example, by
determining the identity of individuals 2310 and 2410 using
available voiceprints thereof. Alternatively, or additionally,
processor 210 may use speech recognition tools or algorithms to
recognize the speech of the individuals.
[0276] In step 2560, process 2500 may include causing selective
conditioning of the first audio signal based on a determination
that the first audio signal is associated with the identified lip
movement associated with the mouth of the individual. Processor 210
may compare the identified lip movement with the first and second
audio signals identified in step 2550. For example, processor 210
may compare the timing of the detected lip movements with the
timing of the voice patterns in the audio signals. In embodiments
where speech is detected, processor 210 may further compare
specific lip movements to phonemes or other features detected in
the audio signal, as described above. Accordingly, processor 210
may determine that the first audio signal is associated with the
detected lip movements and is thus associated with an individual
who is speaking.
[0277] Various forms of selective conditioning may be performed, as
discussed above. In some embodiments, conditioning may include
changing the tone or playback speed of an audio signal. For
example, conditioning may include remapping the audio frequencies
or changing a rate of speech associated with the audio signal. In
some embodiments, the conditioning may include amplification of a
first audio signal relative to other audio signals. Amplification
may be performed by various means, such as operation of a
directional microphone, varying one or more parameters associated
with the microphone, or digitally processing the audio signals. The
conditioning may include attenuating or suppressing one or more
audio signals that are not associated with the detected lip
movement. The attenuated audio signals may include audio signals
associated with other sounds detected in the environment of the
user, including other voices such as a second audio signal. For
example, processor 210 may selectively attenuate the second audio
signal based on a determination that the second audio signal is not
associated with the identified lip movement associated with the
mouth of the individual. In some embodiments, the processor may be
configured to transition from conditioning of audio signals
associated with a first individual to conditioning of audio signals
associated with a second individual when identified lip movements
of the first individual indicates that the first individual has
finished a sentence or has finished speaking.
[0278] In step 2570, process 2500 may include causing transmission
of the selectively conditioned first audio signal to a hearing
interface device configured to provide sound to an ear of the user.
The conditioned audio signal, for example, may be transmitted to
hearing interface device 1710, which may provide sound
corresponding to the first audio signal to user 100. Additional
sounds such as the second audio signal may also be transmitted. For
example, processor 210 may be configured to transmit audio signals
corresponding to sounds 2421, 2422, and 2423. The first audio
signal, which may be associated with the detected lip movement of
individual 2310, may be amplified, however, in relation to sounds
2422 and 2423 as described above. In some embodiments, hearing
interface 1710 device may include a speaker associated with an
earpiece. For example, hearing interface device may be inserted at
least partially into the ear of the user for providing audio to the
user. Hearing interface device may also be external to the ear,
such as a behind-the-ear hearing device, one or more headphones, a
small portable speaker, or the like. In some embodiments, hearing
interface device may include a bone conduction microphone,
configured to provide an audio signal to user through vibrations of
a bone of the user's head. Such devices may be placed in contact
with the exterior of the user's skin, or may be implanted
surgically and attached to the bone of the user.
[0279] Selective Amplification of Speaker of Interest
[0280] The disclosed systems and methods may enable a hearing aid
system to selectively amplify an audio signal and transmit the
amplified audio signal to a hearing interface device configured to
provide sound to an ear of a user. For example, the system may
recognize multiple speakers through analysis of captured images,
but may selectively amplify one of the voices of the detected
speakers. The voice selected for amplification may be based on a
hierarchy or other suitable differentiator. In one example, the
voice of the speaker whom the user is looking at may be amplified.
In another example, the voice of a speaker detected to be looking
toward the user may be amplified. In another example, in case of
speech overlap, the voice of a speaker who already started speaking
but has not finished when another speaker has started speaking may
be selected.
[0281] FIG. 26 illustrates a user wearing an exemplary hearing aid
system. User 2601 may wear a wearable device 2631. Wearable device
2631 may include an image sensor configured to capture images of
the environment of user 2601. As illustrated in FIG. 26, a first
individual 2611 may stand in front of user 2601 and look in the
direction of user 2601. In addition, a second individual 2612 may
also stand in front of user 2601, but look in a direction away from
user 2601. The image sensor of wearable device 2631 may capture one
or more images including first individual 2611 and second
individual 2612.
[0282] FIG. 27 illustrates an exemplary image 2700 of the
environment of user 2601 illustrated in FIG. 26 as may be captured
by an image sensor of wearable device 2631. Image 2700 may include
a presentation 2711 of first individual 2611 and a presentation
2712 of second individual 2612.
[0283] Wearable device 2631 may also include at least one processor
configured to analyze the images captured by the image sensor. The
processor may also identify one or more individuals included in the
images, based on the image analysis. For example, the processor may
receive image 2700 (illustrated in FIG. 27) from the image sensor.
The processor may also identify first individual 2611 and second
individual 2612 included in the image.
[0284] Wearable device 2631 may further include at least one
microphone configured to receive one or more audio signals from the
environment of user 2601. For example, the microphone may be
configured to receive (or detect) a first audio signal associated
with the voice of the first individual 2611 and a second audio
signal associated with the voice of the second individual 2612.
[0285] The processor may detect at least one amplification criteria
indicative of a voice amplification priority between the first
individual and the second individual. Some amplification criteria
may be static, while others may be dynamic. For example, based on
the analysis of image 2700, the processor may detect that first
individual 2611 is looking in the direction of user 2601 and second
individual 2612 is looking in a direction away user 2601, which may
indicate that the voice amplification priority of first individual
2611 should be higher than second individual 2612. The processor
may also selectively amplify the first audio signal, based on the
voice amplification priority.
[0286] Wearable device 2631 may further include a hearing interface
device such as hearing interface device 1710, configured to receive
audio signals and provide sound to an ear of user 2601. For
example, the hearing interface device may receive the amplified
first audio signal and provide sound to user 2601 based on the
amplified first audio signal. In some embodiments, the hearing
interface device may receive the amplified first audio signal and
unprocessed second audio signal, and provide sound to user 2601
based on the amplified first audio signal and second audio
signal.
[0287] FIG. 28 is a flowchart of an exemplary process 2800 for
selectively amplifying an audio signal. At step 2801, the hearing
aid system (e.g., apparatus 110) may receive a plurality of images
of an environment of the user. For example, the hearing aid system
may include a processor (e.g., processor 210) configured to receive
images of the environment of the user captured by an image sensor
(e.g., image sensor 220). In some embodiments, the image sensor may
be part of a camera included the hearing aid system. By way of
example, as illustrated in FIG. 26, user 2601 may wear a wearable
device 2631 that may include an image sensor configured to capture
images of the environment of the user. The processor of the hearing
aid system may receive the images from wearable device 2631.
[0288] In some embodiments, the processor may be configured to
control the image sensor to capture images. For example, the
processor may detect a gesture performed by the user (a
finger-pointing gesture) and control the image sensor to capture
images based on the detected gesture (e.g., adjusting the field of
view of the image sensor based on the direction of the
finger-pointing gesture). As another example, the hearing aid
system may include a microphone configured to detect (or receive)
audio signals from the environment of the user. The processor may
receive the audio signals from the microphone and detect a voice by
one or more individuals nearby. The processor may control the image
sensor to capture images if a voice is detected.
[0289] In some embodiments, the processor may receive data from and
transmit data to the image sensor over one or more networks via any
known wireless standard (e.g., cellular, Wi-Fi, Bluetooth.RTM.,
etc.), or via near-filed capacitive coupling, other short-range
wireless techniques, or via a wired connection. For example, the
processor may also be configured to receive data (e.g., the
captured images, etc.) from the image sensor via a wireless link
between a transmitter in a housing in which the image sensor is
included and a receiver in a housing in which the processor is
included.
[0290] At step 2803, the processor may analyze one or more images
received from the image sensor and identify that one or more
individuals are included in the images. For example, as illustrated
in FIG. 26, two individuals--first individual 2611 and second
individual 2612--stand in front of user 2601 (first individual 2611
may stand closer to user 2601 than second individual 2612 does).
The image sensor may be configured to capture image 2700
(illustrated in FIG. 27) of the environment of user 2601, including
first individual 2611 and second individual 2612. The processor may
analyze image 2700 and identify in image 2700 a representation 2711
of first individual 2611 and representation 2712 of second
individual 2612. Representation 2711 of first individual 2611 may
appear bigger than representation 2712 of second individual 2612
since first individual 2611 may stand closer to user 2601 than
second individual 2612 does. In some embodiments, the processor may
identify one or more individuals based on object recognition
techniques (e.g., a deep-learning algorithm for recognizing
objects).
[0291] In some embodiments, the processor may recognize one or more
individuals included in the images. For example, the processor may
recognize one of the individuals is a family member or a friend,
based on a human recognition technique (e.g., a deep-learning
algorithm for recognizing an individual). In some embodiments, the
processor may be configured to retrieve information relating to the
recognized individual (e.g., the name of the individual and the
last time the user met the individual). The processor may also
transmit the information to the user via the hearing aid interface
and/or a feedback-outputting unit.
[0292] In some embodiments, the processor may be configured to
determine the visual line (or the look direction) of each of one or
more identified individuals and/or the user, based on the analysis
of the images. For example, an individual may look at the user, and
the processor may determine that the look direction of the induvial
is towards the user, based on the image analysis. As another
example, the processor may determine the look direction of the user
based on the image analysis.
[0293] At step 2805, the processor may receive from at least one
microphone a first audio signal associated with a voice of the
first individual. For example, the hearing aid system may include
one or more microphones configured to detect (or receive) audio
signals from the environment of the user. By way of example,
wearable device 2631 (e.g., illustrated in FIG. 26) may include a
microphone configured to receive a first audio signal associated
with first individual 2611 and may receive a second audio signal
associated with the second individual 2612, who stands in front of
user 2601. The processor may receive the first audio signal from
the microphone. In some embodiments, the processor may receive data
from the microphone over one or more networks via any known
wireless standard (e.g., cellular, Wi-Fi, Bluetooth.RTM., etc.), or
via near-filed capacitive coupling, other short-range wireless
techniques, or via a wired connection. For example, the processor
may also be configured to receive data (e.g., the audio signals,
etc.) from the microphone via a wireless link between a transmitter
in a housing in which the microphone is included and a receiver in
a housing in which the processor is included.
[0294] In some embodiments, the processor may be configured to
control the microphone to detect (or receive) audio signals and/or
transmit the audio signals to the processing device (and/or the
hearing aid interface). For example, the processor may identify one
or more individuals based on the analysis of the audio signals. The
processor may activate the microphone to receive audio signals if
one or more individuals are identified. In some embodiments, if a
speaker is recognized and the audio signal is transmitted to the
processing device, the audio signal associated with the speaker may
be transmitted to the processing device as long as the speaker
keeps speaking (or a pause is less than a threshold). In some
embodiments, the audio signal associated with the speaker may be
transmitted to the processing device as long as the speaker keeps
speaking (or a pause is less than a threshold) even if other
voices, whether recognized or not are captured, to let the user
continuously listen to the speaker. For example, the processor may
be configured to continue causing transmission of the first audio
signal, rather than the second audio signal, to the hearing
interface device configured to provide sound to an ear of the user,
until a pause longer than a predetermined length is detected in
speech associated the voice of the first individual. In some
embodiments, short breaks in speaking, for example, breathing
breaks or pauses for searching for a word may still be considered
as continuous speech. In some embodiments, pauses up to a
predetermined length may be considered as part of continuous
speech, while longer periods may be considered as the end of a
speech by the speaker, such that other speakers can be detected or
amplified or amplified to a different degree.
[0295] In some embodiments, the microphone may include a
directional microphone (e.g., a bi-directional microphone, an
omnidirectional microphone, etc.), a microphone array, or the like,
or a combination thereof. In some embodiments, the processor may be
configured to determine the speaking direction of each of one or
more identified individuals and/or the user, based on the audio
signals received. For example, the microphone may include one or
more directional microphones, and the processor may be configured
to determine the speak direction of an individual based on the
audio signal associated with the individual.
[0296] At step 2807, the processor may receive from the microphone
a second audio signal associated with a voice of the second
individual. By way of example, wearable device 2631, illustrated in
FIG. 26, may include a microphone configured to receive a second
audio signal associated with second individual 2612, who stand in
front of user 2601.
[0297] In some embodiments, the processor may be configured to
receive audio signals from the microphone and recognize the
individual associated with the audio signal received. For example,
the processor may recognize the individual based on the
characteristics of the voice of the individual (e.g., the
individual's voice speed, pitch, etc.). In some embodiments, the
processor may be configured to retrieve information relating to the
recognized individual (e.g., the name of the individual and the
last time met the individual). The processor may also transmit the
information to the user via the hearing aid interface and/or a
feedback-outputting unit.
[0298] In some embodiments, the processor may be configured to
recognize the individual associated with the audio signal based on
analysis of the audio signal and one or more images received from
the image sensor. For example, the processor may determine a first
confidence score for the association of an individual and the audio
signal based on the analysis of the audio signal. The processor may
also determine a second confidence score for the association of the
individual and the audio signal based on the analysis of the one or
more images received from the image sensor (similar to the
recognition process in step 2803). The processor may further
determine an overall confidence score based on the first and second
confidence scores and identify the individual based on the overall
confidence score (e.g., identifying the individual if the overall
confidence score exceeds a threshold). By way of example, the
processor may determine a first confidence score of 9 (out of 10)
for the association of a specific individual and an audio signal
based on the analysis of the audio signal. The processor may also
determine a second confidence score of 2 (out of 10) for the
association of the specific individual and the audio signal based
on the analysis of one or more images received from the image
sensor. The processor may further determine an overall confidence
score of 11 (i.e., 2 plus 9 out of 20 in total) and determine that
this individual is not associated with the audio signal if the
threshold is 16. As another example, the processor may determine
the first confidence score of 9 and determine the second confidence
score of 8. The processor may also determine that the overall
confidence score is 17 and recognize the individual being
associated with the audio signal. In some embodiments, the
processor may determine the overall confidence score based a
weighted first confidence score and/or a weighted second confidence
score.
[0299] In some embodiments, the microphone and the image sensor (or
the wearable camera that includes the image sensor) may be included
in a common housing. For example, wearable device 2631 illustrated
in FIG. 26 may include both the microphone and the wearable camera
in a common housing. Alternatively, the microphone may be included
in a housing different from a housing in which the wearable camera
is installed.
[0300] In some embodiments, the processor may be included in a
common housing with at least one of the microphone and the wearable
camera. For example, the processor may also be included in a common
housing in which both the microphone and the wearable camera are
included. Alternatively, the processor may be included in a
separate housing from a common housing where the microphone and the
wearable camera are installed. The processor may also be configured
to receive data (e.g., the captured images, the detected audio
signals, etc.) from the wearable camera and/or the microphone via a
wireless link between a transmitter in the common housing (in which
the microphone and the wearable camera are included) and receiver
in the second housing (in which the processor is included).
[0301] At step 2809, the processor may detect at least one
amplification criteria indicative of a voice amplification priority
between the first individual and the second individual. The
detection of an amplification criteria may be based on the analysis
of the received images and/or audio signals. For example, the
processor may detect that the first individual stands closer to the
user than the second individual stands based on, for example, the
image analysis. The detection that the user is closer to the first
individual than the second individual may be an amplification
criteria indicative of a voice amplification priority of the first
individual over the second individual.
[0302] In some embodiments, the amplification criteria may include
the position and/or the orientation of the user in relative to the
first and/or second individuals, the look direction of the user,
the look direction of the speaker (e.g., the first individual, the
second individual, etc.), or the like, or a combination thereof.
For example, if the user is detected to be facing more towards the
second individual than the first individual (based on the analysis
of the images and/or audio signals), the processor may detect that
the second individual has a higher voice amplification priority
than the first individual does. Alternatively or additionally, the
amplification criteria may relate to the identity of the first
individual and/or the second individual. For example, the processor
may identify the first individual to be a family member (but does
not recognize the second individual) and determine that the first
individual has a higher voice amplification priority than the
second individual.
[0303] In some embodiments, an amplification criteria may include
the look direction of the user, and the voice amplification
priority between the first and second individuals may be determined
based on whether the look direction of the user correlates with the
first individual or with the second individual. For example, the
processor may determine the look direction of the user based on the
analysis of the images captured by the image sensor, and determine
whether the user looks towards more to the first individual or the
second individual. As another example, the processor may be
configured to detect the look direction of the user by detecting a
representation of the user's chin in at least one of the images and
determining the look based on a detected direction associated with
the user's chin. If the look direction of the user correlates with
the first individual more than the second individual, the processor
may determine that the first individual has a higher voice
amplification priority than the second individual.
[0304] In some embodiments, an amplification criteria may include
the look direction of the speaker (e.g., the first individual, the
second individual, etc.), and voice amplification priority between
the first and second individuals may be determined based on whether
the first individual or the second individual is looking in the
direction of the user based on the look direction of the speaker.
For example, if the processor determines that the first individual
is looking in the direction of the user based on the speaker look
of the first individual and the second individual is looking in a
direction away from the user, the first individual may have a
higher voice amplification priority than the second individual. On
the other hand, if the processor determines that the second
individual is looking in the direction of the user and the first
individual is looking in a direction away from the user, the second
individual may have a higher voice amplification priority than the
first individual. In some embodiments, the processor may be
configured to detect the look direction of the speaker based on his
or her facial characteristics (e.g., the eyes, the orientation of
the face, etc.) determined according to the images captured by the
image sensor.
[0305] In some embodiments, amplification criteria may include a
speaking continuity indicating that a speaker who already started
speaking but has not finished when another speaker has started
speaking. For example, the first individual already started
speaking but has not finished when the second individual has
started speaking. The processor may determine that the first
individual may have a speaking continuity and have a higher
amplification priority than the second individual.
[0306] In some embodiments, amplification criteria may include a
relationship between the user and one of the first individual and
the second individual. For example, the processor may identify the
first individual and/or the second individual (as described
elsewhere in this disclosure) and determine a relationship between
the user and one of the first individual and the second individual.
By way of example, the processor may determine that one of the
individuals is a family member or a friend of the user, and
determine the amplification priority based on the determined
relationship. Exemplary relationship may include family member,
friend, acquaintance, colleague, stranger, or the like, or a
combination thereof. Alternatively or additionally, the
amplification criteria may include a relationship between the first
and second individuals. For example, the processor may determine
that the first individual is a supervisor of the second individual
(i.e., a type of relationship). Alternatively or additionally, the
amplification criteria may include a relationship among the user,
the first individual, and the second individual. For example, the
processor may determine that the first individual is a supervisor
of the user and the second individual. In some embodiments, the
processor may determine the amplification priority based on the
closeness of the relationship of the first individual and/or the
second individual with the user. For example, the processor may
determine that the first individual is an immediate family member
of the user and the second individual is a friend of the user, and
determine that the first individual is closer (in terms of
relationship) to the user than the second individual to the user.
The processor may also determine that the first individual has a
higher amplification priority than the second individual.
Alternatively or additionally, the processor may determine the
amplification priority based on the hierarchy of the determined
relationship. For example, the processor may determine that the
first individual is a supervisor of the second individual (i.e., a
type of relationship) and determined that the first individual has
a higher amplification priority than the second individual.
[0307] At step 2811, the processor may selectively amplify the
first audio signal or the second audio signal, based on the voice
amplification priority. For example, if the first audio signal has
a higher voice amplification priority than the second audio signal,
the processor may amplify the first audio signal. Similarly, if the
second audio signal has a higher voice amplification priority than
the first audio signal, the processor may amplify the second audio
signal.
[0308] In some embodiments, the processor may amplify an audio
signal (the first audio signal or the second audio signal) to a
predetermined sound level. Alternatively or additionally, the
processor may amplify an audio signal by increasing the sound level
by a percentage. Alternatively or additionally, while amplifying an
audio signal, the processor may be configured to attenuating one or
more other audio signals (by, for example, decreasing the sound
level of the other signal(s) to a predetermined sound level or by a
predetermined percentage). For example, if the first audio signal
has a higher voice amplification priority than the second audio
signal, the processor may be configured to amplify the first audio
signal by 50% and attenuate the second audio signal by 50%.
[0309] In some embodiments, the hearing aid system may include an
audio amplification circuit configured to selectively amplify an
audio signal. The audio amplification circuit may receive inputs
from two or more input audio transducers. For example, a first
input audio transducer may receive the first audio signal, and a
second input audio transducer may receive the second audio signal.
The processor may cause the audio amplification circuit to amplify
one of the first audio signal or the second audio signal, based on
their voice amplification priorities. Alternatively or
additionally, the processor may cause the audio amplification
circuit to attenuate the audio signal that has a lower voice
amplification priority.
[0310] At step 2813, the processor may cause transmission of the
selectively amplified audio signal to a hearing interface device.
For example, the processor may cause a transmitter to transmit the
amplified audio signal to a hearing interface device via a wireless
network (e.g., cellular, Wi-Fi, Bluetooth.RTM., etc.), or via
near-filed capacitive coupling, other short-range wireless
techniques, or via a wired connection. Alternatively or
additionally, the processor may cause transmission of the
unprocessed audio signal(s) (and/or the selectively attenuated
audio signal) to the hearing interface device.
[0311] The hearing interface device may also be configured to
deliver sound to an ear of the user based on the amplified audio
signal. For example, the hearing interface device may receive the
amplified audio signal (e.g., amplified first audio signal) and
deliver sound to an ear of the user based on the amplified audio
signal. In some embodiments, the hearing interface device may also
receive one or more unprocessed audio signals and/or one or more
attenuated audio signal. For example, the hearing interface device
may receive amplified first audio signal and unprocessed second
audio signal. The hearing interface device may deliver sound based
on the amplified first audio signal and second audio signal.
[0312] In some embodiments, a hearing interface device may include
a speaker associated with an earpiece. For example, the hearing
interface device may include an in-ear earphone. As another
example, the hearing interface device may include a speaker
included in a wearable device (e.g., wearable device 2631). In some
embodiments, the hearing interface device may include an earphone,
a headphone, a speaker, or the like, or a combination thereof.
[0313] In some embodiments, the hearing interface device may
include a bone conduction microphone.
[0314] Differential Amplification Relative to Voice of Speakerphone
User
[0315] The disclosed systems and methods may enable a hearing aid
system to determine through image analysis that at least one
speaker in a group is participating in a group meeting via
speakerphone (e.g., by receiving at least one voice signal even
where image analysis indicates no visible speakers are present in a
group). Such a voice signal may originate from a person
participating in a meeting by speakerphone or from a person outside
of the field-of-view (FOV) of a wearable camera, for example a
speaker sitting in the back seat of a car while the wearable camera
is front-facing. In such scenarios, the voice signal may be weaker
than voice signals received from individuals physically present in
a group or in front of the user (e.g., with an unimpeded path to
the user's sound collection microphone). The audio signals
determined to be received from a source different from the imaged
individuals may be amplified differently (e.g., using a higher
gain) relative to audio signals received from imaged individuals.
In an embodiment, the system may detect the presence of a
speakerphone participant, at least in part, through detection in a
captured image of a speakerphone device or similar device present
in the FOV of the system camera.
[0316] In some embodiments, the system may automatically identify
individuals present in a group discussion (e.g., via facial
recognition, voice recognition, or both) and record the discussion
participants in a database. The system may also determine the
identity of at least one person participating in the discussion by
phone (or outside the camera FOV) through voice recognition or
based on other criteria, such as meeting invite records, prior
known associations, etc. The system may record the identities of
the participants. In some embodiments, the system may amplify the
voice of a person that previously appeared in a camera FOV but has
exited the FOV, and later speaks (e.g., during a car ride, or in a
user's home, etc.). In some embodiments, the system may also
amplify certain sound signals. For example, the system may amplify
a fire alarm, a siren, crying by a kid, a voice warning (e.g.,
"Mayday!"). Alternatively or additionally, some (predetermined,
recognized or not) sounds, whether recognized or not, or
predetermined may be amplified and transmitted at a delay. For
example, in an airport when there is an announcement about a
flight, the system may realize that this is an important
announcement only after the flight number is mentioned. The system
may play the whole announcement even though that voice is not of
anyone known to the user.
[0317] FIG. 29 illustrates an exemplary hearing aid system. User
2901 may wear a wearable device 2931. Wearable device 2931 may
include an image sensor configured to capture images of the
environment of user 2901. As illustrated in FIG. 29, user 2901 may
sit by one side of a table. A first individual 2911 and a second
individual 2912 may sit by another side of the table. The image
sensor of wearable device 2931 may capture one or more images of
the environment of user 2901, including first individual 2911,
second individual 2912, and a speakerphone 2921.
[0318] FIGS. 30A and 30B illustrate exemplary images 3000A and
3000B of the environment of user 2901 illustrated in FIG. 29. Image
3000A may include a representation 3011 of first individual 3011, a
representation 3012 of second individual 3012, and a representation
3021 of speakerphone 2921. Image 3000B may include representation
3012 of second individual 3012 and representation 3021 of
speakerphone 2921 (the first individual may be out of the FOV of
the camera). Wearable device 2931 may also include at least one
processor configured to analyze the images captured by the image
sensor. The processor may also identify a representation of one or
more individuals and one or more objects included in the images,
based on the image analysis. For example, the processor may receive
image 3000A and/or 3000B (illustrated in FIGS. 30A and 30B) from
the image sensor and identify representations of first individual
2911, second individual 2912, and speakerphone 2921 included in the
image. In some embodiments, the processor may be programmed to
perform one or more steps of process 3110, process 3130, and/or
process 3150 (illustrated in FIGS. 31A, 31B, and 31C,
respectively).
[0319] In some embodiments, wearable device 2931 may be configured
to automatically identify one or more individuals, based on the
images, the audio signal(s) detected, another type of data, or the
like, or a combination thereof. For instance, wearable device 2931
may automatically identify first individual 2911 and second
individual 2912 based on the images using facial recognition
technologies. Alternatively or additionally, wearable device 2931
may automatically identify an individual based on voice recognition
(e.g., the voice print of the individual) associated with an audio
signal detected. For instance, wearable device 2931 may
automatically identify an individual who is not in the room with
user 2901 and is participating in a conference call via
speakerphone 2921, based on a detected audio signal associated with
the individual. Alternatively or additionally, wearable device 2931
may automatically identify an individual based on a calendar invite
associated with the user or prior known associations of the user.
For example, wearable device 2931 may receive data relating to a
calendar invite, which may include the identity of one or more
participants. Wearable device 2931 may identify an individual as
one of the participants included in the calendar invite. In some
embodiments, wearable device 2931 may further record the
identification of the one or more individuals in a database.
[0320] Wearable device 2931 may further include at least one
microphone configured to receive one or more audio signals from the
environment of user 2901. For example, the microphone may be
configured to receive (or detect) an audio signal associated with
the first individual 2911 and/or the second individual 2912 and/or
additional audio such as background noise. The microphone may also
be configured to receive (or detect) an audio signal associated
with the speakerphone 2921 (e.g., the voice of a third individual
participating in the conference through the speakerphone 2921).
[0321] In some embodiments, the microphone may include a
directional microphone (e.g., a bi-directional microphone, an
omnidirectional microphone, etc.), a microphone array, or the like,
or a combination thereof. In some embodiments, the processor may be
configured to determine the speak direction of each of one or more
identified individuals and/or the user, based on the audio signals
received. For example, the microphone may include one or more
directional microphones, and the processor may be configured to
determine the speak direction of an individual based on the audio
signal associated with the individual.
[0322] Wearable device 2931 may also determine, based on analysis
of the images, whether an audio signal received is associated with
a voice of the one or more individuals detected in the images. For
example, wearable device 2931 may receive a first audio signal and
determine that the first audio signal is not associated with any of
the individuals (e.g., first individual 2911 and second individual
2912) identified in the images based on analysis of the images.
Additionally, wearable device 2931 may receive a second audio
signal and determine that the second audio signal is associated
with a voice of first individual 2911. Wearable device 2931 may
further determine the sources of the audio signals based on the
images and/or the audio signals. For example, wearable device 2931
may detect lip movements associated with first individual 2911
based on the image analysis. Wearable device 2931 may also
determine that the detected lip movements correspond to the second
audio signal and determine the source of the second audio signal to
be first individual 2911. As another example, wearable device 2931
may determine that an audio signal originated from a speaker. In
some embodiments, the speaker may include a speakerphone, a
network-connected speaker (e.g., Bluetooth or WiFi speaker), a
wired speaker, a cell phone, or the like, or a combination thereof.
By way of example, wearable device 2931 may determine that the
speaker is included in a speakerphone by detecting, through
analysis of one or more of the images, a representation of a device
recognized as a speakerphone.
[0323] Wearable device 2931 may further cause a first amplification
of the first audio signal and a second amplification of the second
audio signal. The first amplification may differ from the second
amplification in at least one aspect. For example, wearable device
2931 may amplify the first audio signal by a first gain level and
amplify the second audio signal by a second gain level. In some
embodiments, the first gain level may be greater than the second
gain level.
[0324] Wearable device 2931 may be in communication with a hearing
interface device (e.g., an earphone) configured to receive audio
signals and provide sound to an ear of user 2901. For example,
wearable device 2931 may cause transmission of at least one of the
first audio signal, amplified according to the first amplification,
and the second audio signal, amplified according to the second
amplification, to a hearing interface device configured to provide
sound to an ear of user 2901. For example, the processor may cause
a transmitter to transmit at least one of the first audio signal,
amplified according to the first amplification, and the second
audio signal, amplified according to the second amplification to a
hearing interface device via a wireless network (e.g., cellular,
Wi-Fi, Bluetooth.RTM., etc.), or via near-field capacitive
coupling, other short-range wireless techniques, or via a wired
connection.
[0325] In some embodiments, a hearing interface device may include
a speaker associated with an earpiece. For example, the hearing
interface device may include an in-the-ear, in-the-canal,
completely-in-canal, behind-the-ear, on-the-ear, receiver-in-canal,
open fit, or various other styles of earphones. As another example,
the hearing interface device may include a speaker included in a
wearable device (e.g., wearable device 2631). In some embodiments,
the hearing interface device may include an earphone, a headphone,
a speaker, or the like, or a combination thereof. In some
embodiments, the hearing interface device may include a bone
conduction microphone.
[0326] In some embodiments, the microphone and the image sensor (or
the wearable camera that includes the image sensor) may be included
in a common housing. For example, wearable device 2931 illustrated
in FIG. 29 may include both the microphone and the wearable camera
in a common housing. Alternatively, the microphone may be included
in a housing different from a housing in which the wearable camera
is installed.
[0327] In some embodiments, the processor may be included in a
common housing with at least one of the microphone and the wearable
camera. For example, the processor may also be included in a common
housing in which both the microphone and the wearable camera are
included. Alternatively, the processor may be included in a
separate housing from a common housing where the microphone and the
wearable camera are installed. The processor may also be configured
to receive data (e.g., the captured images, the detected audio
signals, etc.) from the wearable camera and/or the microphone via a
wireless link between a transmitter in the common housing (in which
the microphone and the wearable camera are included) and receiver
in the second housing (in which the processor is included).
[0328] FIG. 31A is a flowchart of an exemplary process for
selectively amplifying audio signals. At step 3111, the hearing aid
system may receive a plurality of images captured by a camera. For
example, the hearing aid system may include a processor (e.g.,
processor 210) configured to receive images of the environment of
the user captured by an image sensor (e.g., image sensor 220). In
some embodiments, the image sensor may be part of a camera included
the hearing aid system. By way of example, as illustrated in FIG.
29, user 2901 may wear a wearable device 2931 that may include an
image sensor configured to capture images of the environment of the
user. The processor of the hearing aid system may receive the
images from wearable device 2931.
[0329] In some embodiments, the hearing aid system may control the
image sensor to capture images. For example, the processor may
detect a gesture performed by the user (a finger-pointing gesture)
and control the image sensor to capture images based on the
detected gesture (e.g., adjusting the field of view of the image
sensor based on the direction of the finger-pointing gesture). As
another example, the hearing aid system may include a microphone
configured to detect (or receive) audio signals from the
environment of the user. The processor may receive the audio
signals from the microphone and detect a voice by one or more
individuals nearby. The processor may control the image sensor to
capture images if a voice is detected.
[0330] In some embodiments, the processor may receive data from or
transmit data to the image sensor over one or more networks via any
known wireless standard (e.g., cellular, Wi-Fi, Bluetooth.RTM.,
etc.), or via near-field capacitive coupling, other short-range
wireless techniques, or via a wired connection. For example, the
processor may also be configured to receive data (e.g., the
captured images, etc.) from the image sensor via a wireless link
between a transmitter in a housing in which the image sensor is
included and a receiver in a housing in which the processor is
included.
[0331] At step 3113, the hearing aid system may identify a
representation of one or more individuals in the plurality of
images. For example, the processor may identify a representation of
first individual 2911 and second individual 2912 in the images. For
instance, the processor may analyze image 3000A illustrated in FIG.
30A and identify representation 3011 of first individual 2911 and
representation 3012 of second individual 2912. In some embodiments,
the processor may also identify a representation of one or more
objects included in the images. For example, the processor may
identify in image 3000A representation 3021 of speakerphone 2921
(illustrated in FIG. 29).
[0332] In some embodiments, the processor may be configured to
automatically identify the one or more individuals, based on the
images, the audio signal(s) detected, another type of data, or the
like, or a combination thereof. For instance, the processor may
automatically identify first individual 2911 and second individual
2912 based on the images using facial recognition technologies.
Alternatively or additionally, the processor may automatically
identify an individual based on voice recognition (e.g., the voice
print of the individual) associated with an audio signal detected.
Alternatively or additionally, the processor may automatically
identify an individual based on a calendar invite associated with
the user or prior known associations of the user. For example, the
processor may receive data relating to a calendar invite, which may
include the identity of one or more participants. The processor may
identify first individual 2911 as one of the participants included
in the calendar invite. In some embodiments, the processor may
further record the identification of the one or more individuals in
a database.
[0333] At step 3115, the hearing aid system may receive from the at
least one microphone a first audio signal associated with a voice.
For example, the hearing aid system may include a microphone
configured to receive (or detect) audio signals from the
environment of user 2901, including a first audio signal associated
with a voice.
[0334] At step 3117, the hearing aid system may determine, based on
analysis of the plurality of images, that the first audio signal is
not associated with a voice of any of the one or more individuals.
For example, the processor may analyze the images to detect the
facial expression (e.g., lip movements) of the individual(s)
detected in the images. The processor may determine the first audio
signal is not associated with a voice of any of the one or more
individuals by analyzing detected lip movements associated with
mouths of first individual 2911 and/or second individual 2912, and
determine that the first audio signal does not correspond to the
detected lip movements associated with mouths of first individual
2911 and/or second individual 2912. The first audio signal may be
associated with a voice of an individual who is outside of the FOV
of the camera (e.g., an individual participating in a conference
call through speakerphone 2921 or an individual who is in the room
but sits far away from user 2901).
[0335] In some embodiments, the processor may identify the source
of the first audio signal. For example, the processor may
automatically identify an individual based on voice recognition
(e.g., the voice print of the individual) associated with an audio
signal detected. By way of example, the processor may automatically
identify an individual who is not in the room with user 2901 and is
participating a conference call via speakerphone 2921, based on the
analysis of the first audio signal. Alternatively or additionally,
the processor may automatically identify an individual based on a
calendar invite associated with the user or prior known
associations of the user. For example, the processor may receive
data relating to a calendar invite, which may include the identify
of one or more participants. The processor may identify an
individual as one of the participants included in the calendar
invite. In some embodiments, the processor may further record the
identification of the one or more individuals in a database. The
participant may be captured earlier and then disappeared from the
image (maybe went to another room)
[0336] At step 3119, the hearing aid system may receive from the at
least one microphone a second audio signal associated with a voice.
The hearing aid system may receive the first audio signal and the
second audio signal at the same time or at different times. In some
embodiments, at least portion of the first audio signal may overlap
with a portion of the second audio signal. The voices may be
separate by any voice separation technique, for example using
periods in which only one speaker speaks, as detailed above.
[0337] At step 3121, the hearing aid system may determine, based on
analysis of the plurality of images, that the second audio signal
is associated with a voice of one of the one or more individuals.
For example, the processor may determine the second audio signal is
associated with first individual 2911 (and/or second individual
2912) by analyzing detected lip movements associated with mouths of
first individual 2911 (and/or second individual 2912) and
determining that the second audio signal corresponds to the
detected lip movements associated with a mouth of first individual
2911 (and/or second individual 2912).
[0338] In some embodiments, the processor may be configured to
automatically identify one or more individuals associated with the
first audio signal and/or the second audio signal, based on the
images, the audio signal(s) detected, another type of data, or the
like, or a combination thereof. For instance, the processor may
automatically identify first individual 2911 (and/or second
individual 2912) who is associated with the second audio signal,
based on the images using facial recognition technologies.
Alternatively or additionally, the processor may automatically
identify an individual based on voice recognition (e.g., the voice
print of the individual) associated with an audio signal detected.
Alternatively or additionally, the processor may automatically
identify an individual based on a calendar invite associated with
the user or prior known associations of the user. For example, the
processor may receive data relating to a calendar invite, which may
include the identity of one or more participants. The processor may
identify an individual as one of the participants included in the
calendar invite. In some embodiments, the processor may further
record the identification of the one or more individuals in a
database.
[0339] At step 3123, the hearing aid system may cause a first
amplification of the first audio signal and a second amplification
of the second audio signal. The first amplification may differ from
the second amplification in at least one aspect. For example, the
processor may amplify the first audio signal by a first gain level
and amplify the second audio signal by a second gain level. In some
embodiments, the first gain level may be greater than the second
gain level.
[0340] In some embodiments, the processor may amplify the first
audio signal to a first predetermined sound level and the second
audio signal to a second predetermined sound level. The first
predetermined sound level may be lower than, greater than, or the
same as the second predetermined sound level. Alternatively or
additionally, the processor may amplify the first audio signal by
increasing the sound level by a first percentage and amplify the
second audio signal by increasing the sound level by a second
percentage.
[0341] At step 3125, the hearing aid system may cause transmission
of at least one of the first audio signal, amplified according to
the first amplification, and the second audio signal, amplified
according to the second amplification, to a hearing interface
device configured to provide sound to an ear of the user. For
example, the processor may include a transmitter configured to
transmit the amplified audio signal(s) (e.g., amplified first audio
signal, amplified second audio signal, etc.) to a hearing interface
device via a wireless network (e.g., cellular, Wi-Fi,
Bluetooth.RTM., etc.), or via near-field capacitive coupling, other
short-range wireless techniques, or via a wired connection. A
hearing interface device may include a speaker associated with an
earpiece. For example, the hearing interface device may include an
in-ear earphone. As another example, the hearing interface device
may include a speaker included in a wearable device (e.g., wearable
device 2931). In some embodiments, the hearing interface device may
include an earphone, a headphone, a speaker, or the like, or a
combination thereof.
[0342] FIG. 31B is a flowchart of an exemplary process 3130 for
selectively amplifying audio signals. At step 3131, the hearing aid
system (e.g., apparatus 110) may receive a first plurality of
images. For example, the hearing aid system may include a processor
(e.g., processor 210) configured to receive images of the
environment of the user captured by an image sensor (e.g., image
sensor 220). In some embodiments, the image sensor may be part of a
camera included in the hearing aid system. By way of example, as
illustrated in FIG. 29, user 2901 may wear the processor that may
include an image sensor configured to capture images of the
environment of the user. The processor of the hearing aid system
may receive the images from wearable device 2931.
[0343] At step 3133, the hearing aid system may identify a
representation of an individual in the first plurality of images.
In some embodiments, the hearing aid system may identify a
representation of an individual (and or an object) in the first
plurality of images using a method similar to that of step 3113 of
process 3110 described above. For example, the processor may be
configured to analyze image 3000A and identify representation 3011
of first individual 2911 and/or representation 3012 of second
individual 2912 in image 3000A based on the image analysis. In some
embodiments, the processor may also automatically identify the
individual and record the identification of the individual into a
database as described elsewhere in this disclosure.
[0344] At step 3135, the hearing aid system receive from the at
least one microphone a first audio signal representative of a
voice. For example, the hearing aid system may include a microphone
configured to receive (or detect) audio signals from the
environment of user 2901, including a first audio signal associated
with a voice. In some embodiments, the microphone may include a
directional microphone (e.g., a bi-directional microphone, an
omnidirectional microphone, etc.), a microphone array, or the like,
or a combination thereof.
[0345] In some embodiments, the microphone and the image sensor (or
the wearable camera that includes the image sensor) may be included
in a common housing. Alternatively, the microphone may be included
in a housing different from a housing in which the wearable camera
is installed. In some embodiments, the processor may be included in
a common housing with at least one of the microphone and the
wearable camera. For example, the processor may also be included in
a common housing in which both the microphone and the wearable
camera are included. Alternatively, the processor may be included
in a separate housing from a common housing where the microphone
and the wearable camera are installed. The processor may also be
configured to receive data (e.g., the captured images, the detected
audio signals, etc.) from the wearable camera and/or the microphone
via a wireless link between a transmitter in the common housing (in
which the microphone and the wearable camera are included) and
receiver in the second housing (in which the processor is
included).
[0346] At step 3137, the hearing aid system may determine, based on
analysis of the first plurality of images, that the first audio
signal representative of a voice is associated with the individual.
In some embodiments, the hearing aid system may determine, based on
analysis of the first plurality of images, that the first audio
signal representative of a voice is associated with the individual
using a method similar to that of step 3121 of process 3110
described above. For example, the processor may determine the first
audio signal is associated with first individual 2911 (and/or
second individual 2912) by analyzing detected lip movements
associated with mouths of first individual 2911 (and/or second
individual 2912) and determining that the first audio signal
corresponds to the detected lip movements associated with a mouth
of first individual 2911 (and/or second individual 2912).
[0347] At step 3139, the hearing aid system may selectively amplify
the first audio signal over other audio signals received from the
at least one microphone representative of sounds from sources other
than the individual. For example, the processor may amplify the
first audio signal by a first gain level. Alternatively or
additionally, the processor may amplify the first audio signal to a
first predetermined sound level. Alternatively or additionally, the
processor may amplify the first audio signal by increasing the
sound level by a percentage.
[0348] At step 3141, the hearing aid system may receive a second
plurality of images captured by the camera. In some embodiments,
the hearing aid system may receive the second plurality of images
after the first plurality of images. For example, the processor may
receive the first plurality of images captured by the camera during
a first period of time and receive the second plurality of images
captured by the camera during a second period of time. The hearing
aid system may receive the second plurality of images using a
method similar to step 3131 described above. For example, the
hearing aid system may receive image 3000B illustrated in FIG. 30B
(as one of the second plurality of images).
[0349] At step 3143, the hearing aid system may receive from the at
least one microphone a second audio signal representative of a
voice associated with the individual. In some embodiments, the
hearing aid system may receive the second audio signal
representative of a voice after the first audio signal. For
example, the second audio signal may be received from speaker
(e.g., speakerphone 2921) through which the individual speaks
(e.g., the individual is speaking via speakerphone 2921 in a
telephonic call). As another example, the second audio signal may
be received from the individual directly. In some embodiments, the
hearing aid system may receive the second audio signal using a
method similar to step 3135 described above.
[0350] At step 3145, the hearing aid system may determine, based on
analysis of the second plurality of images, that the individual is
not represented in the second plurality of images. For example, the
hearing aid system may determine, based on analysis of image 3000B
illustrated in FIG. 30B, first individual 2911 may be outside of
the FOV of the camera when image 3000B is captured (e.g., having
left the room or being outside of the FOV despite remaining in the
room). The processor may analyze the second plurality of images and
determine that the individual is not represented in the second
plurality of images.
[0351] At step 3147, the hearing aid system may selectively amplify
the second audio signal over other received audio signals
representative of sounds from sources other than the individual. In
some embodiments, the processor may selectively amplify the second
audio signal by a second gain level. Alternatively or
add-itionally, the processor may amplify the second audio signal to
a second predetermined sound level. Alternatively or additionally,
the processor may amplify the second audio signal by increasing the
sound level by a percentage.
[0352] At step 3149, the hearing aid system may cause transmission
of at least one of the selectively amplified first audio signal or
the selectively amplified second audio signal to a hearing
interface device. Transmission of an amplified audio signal to the
hearing interface device is described elsewhere in this disclosure.
For example, the processor may cause a transmitter to transmit at
least one of the selectively amplified first audio signal or the
selectively amplified second audio signal to the hearing interface
device via a wireless network (e.g., cellular, Wi-Fi,
Bluetooth.RTM., etc.), or via near-field capacitive coupling, other
short-range wireless techniques, or via a wired connection.
[0353] In some embodiments, the hearing interface device may
include a speaker associated with an earpiece. For example, the
hearing interface device may include an in-ear earphone. As another
example, the hearing interface device may include a speaker
included in a wearable device (e.g., wearable device 2631). In some
embodiments, the hearing interface device may include an earphone,
a headphone, a speaker, or the like, or a combination thereof. In
some embodiments, the hearing interface device may include a bone
conduction microphone.
[0354] FIG. 31C is a flowchart of an exemplary process for
selectively amplifying audio signals. At step 3151, the hearing aid
system may receive a plurality of images. The hearing aid system
may receive a plurality of images based on a method similar to step
3111 of process 3110 described above. For example, user 2901 may
wear wearable device 2931 that may include an image sensor
configured to capture images of the environment of the user. The
processor of the hearing aid system may receive the images from
wearable device 2931.
[0355] At step 3153, the hearing aid system may identify a
representation of one or more individuals in the plurality of
images. The hearing aid system may identify a representation of one
or more individuals in the plurality of images based on a method
similar to step 3113 of process 3110 described above. For instance,
the processor may analyze image 3000A illustrated in FIG. 30A, and
identify representation 3011 of first individual 2911 and
representation 3012 of second individual 2912. In some embodiments,
the processor may also identify a representation of one or more
objects included in the images. For example, the processor may
identify in image 3000A representation 3021 of speakerphone 2921
(illustrated in FIG. 29).
[0356] At step 3155, the hearing aid system may receive from the at
least one microphone a first audio signal associated with a voice.
In some embodiments, the hearing aid system may receive from the at
least one microphone a first audio signal based on a method similar
to step 3115 of process 3110 described above. For example, the
processor may include a microphone configured to receive (or
detect) audio signals from the environment of user 2901, including
a first audio signal associated with a voice.
[0357] At step 3157, the hearing aid system may determine, based on
analysis of the images, that the first audio signal is not
associated with a voice of any of the one or more individuals. In
some embodiments, the determination may be based on a method
similar to step 3117 of process 3110 described above. For example,
the processor may analyze the images to detect the facial
expression (e.g., lip movements) of the individual(s) detected in
the first plurality of images. The processor may determine the
first audio signal is not associated with a voice of any of the one
or more individuals is made by analyzing detected lip movements
associated with mouths of first individual 2911 and/or second
individual 2912, and determine that the first audio signal does not
correspond to the detected lip movements associated with mouths of
first individual 2911 and/or second individual 2912.
[0358] At step 3159, the hearing aid system may determine, based on
analysis of the audio signal, that the audio signal is associated
with at least one indicator that the audio signal is related to a
public announcement. For example, the processor may analyze the
received audio signal to determine the content associated with the
audio signal. The processor may also determine that the audio
signal is related to a public announcement based on the content. A
public announcement may include a communication intended for a
group of people, of which the device user may be a part (e.g., a
gate announcement broadcasted at an airport). As another example, a
public announcement may be a call for help (e.g., Mayday,
etc.).
[0359] In some embodiments, the at least one indicator that the
audio signal is related to a public announcement may include a
recognized sound, word or phrase associated with the audio signal.
For example, the processor may recognize one or more words or
phrases that are associated with an airport announcement (e.g., a
flight number), and determine that the audio signal is related to a
public announcement based on the recognized word or phrase. As
another example, the audio signal may include a word (or phrase)
such as "help," "watch out," "attention," "announcement" (or
similar words or phrases in other languages), or the like, or a
combination thereof. The processor may analyze the audio signal and
recognize such word (or phrase) and determine that the audio signal
is related to a public announcement based on the recognized word
(or phrase). Alternatively or additionally, the at least one
indicator that the audio signal is related to a public announcement
may include a volume level of the audio signal relative to an
ambient noise level, which may indicate that the audio signal
relates to a yell, scream, a public announcement over a
loudspeaker, or the like, or a combination thereof. For example,
the processor may determine that the volume level of the audio
signal is greater than the ambient noise level by a threshold, and
determine that the audio signal may be related to a public
announcement or an event that needs attention. Alternatively or
additionally, the at least one indicator that the audio signal is
related to a public announcement includes at least one signal
component associated with the audio signal indicative of production
of the audio signal by a loudspeaker. For example, the audio signal
may be related to a broadcast over one or more loudspeakers, which
may include one or more signal characteristics indicating
amplification of the voice or reproduction of the voice over a
public address system.
[0360] At step 3161, the hearing aid system may cause selective
amplification of the audio signal based on the determination that
the audio signal is associated with at least one indicator that the
audio signal relates to a public announcement. For example, the
processor may amplify the audio signal associated with a public
announcement. In some embodiments, the processor may amplify the
audio signal to a predetermined sound level. Alternatively or
additionally, the processor may amplify the audio signal by
increasing the sound level by a percentage. Alternatively or
additionally, while amplifying an audio signal, the processor may
be configured to attenuating one or more other audio signals (by,
for example, decreasing the sound level of the other signal(s) to a
predetermined sound level or by a predetermined percentage). For
example, the processor may be configured to amplify the audio
signal associated with a public announcement by 50% and attenuate
one or more other audio signals by 50%.
[0361] In some embodiments, the processor may determine whether the
audio signal relating to a public announcement (e.g., a public
announcement at an airport) is relevant to the user and may
selectively amplify the audio signal based on the result of the
determination. For example, the processor may determine that the
audio signal relates to a flight that is irrelevant to the user and
may not amplify the audio signal accordingly. As another example,
the processor may determine that the public announcement associated
with the audio signal relates to a gate change of the user's
flight. The processor may also selectively amplify the audio
signal. In some embodiments, the processor may determine whether
airport announcement is relevant to the user based on automatic
review of a calendar entry or reservation notice stored on a mobile
device associated with the user. For example, the processor may
access the data relating to a calendar entry or reservation notice
stored on a mobile device associated with the user and determine
that the flight information (e.g., the flight number) relating to
the flight that the user is going to take based on the accessed
data. The processor may also determine whether a public
announcement associated with the audio signal is relevant to the
user based on the flight information and the audio signal (e.g.,
the content of the message associated with the audio signal).
[0362] In some embodiments, the hearing aid system may capture one
or more audio signals received during a moving time window of a
predetermined length, and the processor may be programmed to cause
selective amplification and transmission of a portion of the audio
signal received within the moving time window but prior to the
determination that the audio signal is related to a public
announcement. For example, the hearing aid system may recognize
that a stream of voice communications (in the form of one or more
audio signals) includes a public announcement. The recognition may
be after the announcement begins. The processor may use a moving
time window of the captured audio signal(s) to go back to the
beginning of the public announcement and extract information
relating to the full announcement from within the time window and
selectively amplify that full announcement (in the form of one or
more audio signals) for the user. The amplified audio signal(s) may
be transmitted to the user time-delayed relative to the original
announcement.
[0363] At step 3163, the hearing aid system may cause transmission
of the selectively amplified audio signal to a hearing interface
device. For example, the processor may cause a transmitter to
transmit the amplified audio signal to a hearing interface device
via a wireless network (e.g., cellular, Wi-Fi, Bluetooth.RTM.,
etc.), or via near-field capacitive coupling, other short-range
wireless techniques, or via a wired connection. Alternatively or
additionally, the processor may cause transmission of the
unprocessed audio signal(s) (and/or the selectively attenuated
audio signal) to the hearing interface device.
[0364] Selectively Conditioning Audio Signals
[0365] In accordance with various embodiments of the disclosure, a
wearable apparatus, such as apparatus 110, may be configured to use
audio information in addition to image information. For example,
apparatus 110 may detect and capture sounds in an environment of
the user (e.g., user 100), via one or more microphones. Apparatus
110 may use this audio information instead of, or in combination
with, image and/or video information to determine situations,
identify persons, perform activities, or the like. The image and/or
video information may supplement the audio information in various
situations for people wearing a hearing aid system. For example,
people that use a hearing aid often find that the hearing aid does
not perform optimally in a crowded environment. In such cases,
various environmental sounds may be amplified and impede a user who
wears the hearing aid (e.g., user 100) from clearly distinguishing
sounds that are directly relevant to user 100, such as
conversational words or sounds from a person communicating with
user 100. In such cases, image data may be used to identify an
individual relevant to user 100 (e.g., an individual in
conversation with user 100).
[0366] In accordance with an embodiment of the disclosure, a
hearing aid system is provided. The hearing aid system may include
a wearable camera configured to capture a plurality of images from
an environment of user 100. In various embodiments, the hearing aid
system may include at least one microphone configured to capture
sounds from an environment of the user. In some embodiments, the
hearing aid system may include more than one microphone. In an
example embodiment, the hearing aid system may include a first
microphone for capturing audio signals in a first wavelength range
and a second microphone for capturing audio signals in a second
wavelength range.
[0367] The hearing aid system may include at least one processor
programmed to receive the plurality of images captured by the
wearable camera and identify a representation of at least one
individual in at least one of the plurality of images. The
processor may be configured to use a computer-based model to
extract an image of a person from the received image. For example,
the processor may use a neural network model (e.g., a convolutional
neural network (CNN)), to recognize an image of a person in the
received image. In an example embodiment, the hearing aid system
may be configured to capture an image in a direction normal to a
face of user 100 (e.g., determined based on a direction associated
with a chin of the user, which direction can be normal to the chin
of user 100) to discern a speaker in the captured image.
[0368] The hearing aid system may be incorporated into apparatus
110, or in some embodiments, apparatus 110 may constitute the
hearing aid system. As previously described, in connection with
FIG. 2, apparatus 110 may transfer or receive data to/from server
250 via network 240. In an example embodiment, an image of a person
in the received image may be transferred to server 250 for analysis
of the image. Server 250 may include a processor configured to
access a database associated with server 250 containing various
images of people that are related to user 100 and compare these
images with the one or more images of a person transferred to
server 250 by apparatus 110.
[0369] In an example embodiment, the database of server 250 may
select images of friends of user 100, relatives of user 100,
co-workers of user 100, persons whom user 100 has encountered in
the past, and the like for comparison with the one or more images
of a person captured by the hearing aid system. In some
embodiments, the hearing aid system may access to a global
positioning system (GPS) and may determine the location of user
100. For example, the hearing aid system may include a GPS system,
or it may communicate with a mobile device (e.g., smartphone,
tablet, laptop, etc.) of user 100 that includes a GPS system (or
alternative system for determining position of the mobile device,
such as Wi-Fi, local network, etc.) to obtain location data (e.g.,
coordinates of user 100, address of user 100, IP address of the
mobile device of user 100, etc.). The hearing aid system may
communicate the location of user 100 to server 250. In an example
embodiment, server 250 may be configured to select from a database
(e.g., stored in server 250) images of people who are likely to be
found at the location of user 100. For example, when user 100 is
located at a work site, images of co-workers may be selected
first.
[0370] Additionally, or alternatively, the hearing aid system may
communicate to server 250 a time when the images of an environment
of user 100 were captured by the wearable camera. In an example
embodiment, server 250 may be configured to select from a database
(e.g., stored in server 250) images of people who are likely to be
found at the location of user 100 at the communicated time. For
example, when user 100 is located at home, and the time corresponds
to dinner time, the images of relatives may be selected first.
[0371] In various embodiments, the image of a person obtained from
captured images by the wearable camera of the hearing aid system
may be compared by a processor of server 250 with various images
selected from the database of server 250 using any suitable
approaches. For example, images may be compared by using neural
networks such as CNN, or any other suitable computer-based methods.
In some embodiments, a computer-based model may assign a likelihood
indicating to what degree the image of the person obtained from
captured images matches with at least one image found in the
database of server 250. In an example embodiment, the likelihood
may be a probability of the image of the person obtained from
captured images matching with at least one image found in the
database of server 250 and may be in the range of values from zero
to one.
[0372] In various embodiments, an image in the database of server
250 may have an associated data record that can be stored in the
database of server 250 in association with the related image. For
example, the image from the database may have a data record
associated with a person, and the data record may include a
person's name, a relationship to user 100, dates and times the
person met user 100, and the like. In some cases, one data record
may be associated with multiple images located in the database of
server 250. A data record may be retrieved from the database for
one or more associated images. For example, server 250 may be
configured to retrieve a data record for the one or more associated
images using a processor. Additionally, or alternatively, one or
more images may be retrieved from the database for the associated
data record. In an example embodiment, the image of a person
obtained from captured images may be compared with more than one
image from the database of server 250 that corresponds to the same
data record to establish the likelihood. In various embodiments, if
the likelihood is above a predetermined threshold value, the
hearing aid system may establish that the image of the person
obtained from captured images matches the data record from the
database.
[0373] In an example embodiment, data records for images stored in
the database of server 250 may be linked. For example, a data
record for one person may be linked with a data record for another
person, where a link may include any suitable relationship
information between the linked data records. In some cases, the
link may be used to define the relationship between people whose
data records are stored in the database. For example, the people
may be defined as co-workers, friends, competitors, neighbors,
teammates, admirers of the same product, person, singer, actor, and
the like. In various embodiments, server 250 may use links between
data records to re-evaluate the likelihood that the person
identified from captured images matches the image of an individual
found in the database of server 250. For example, if a data record
for an image of the individual found in the database of server 250
includes a link to a data record of user 100, the likelihood value
may be increased. For example, if the link indicates that user 100
is a co-worker of the individual, and user 100 is located at a work
site, the likelihood value may be increased. In some embodiments, a
first encounter with a first individual (e.g., a coworker) may
affect the likelihood value that a second individual (e.g., another
coworker) identified from captured images during a second
encounter, matches a data record for an individual found in the
database of server 250.
[0374] While the discussion above describes using server 250 for
analyzing images captured by a wearable device of user 100,
additionally, or alternatively, a processor of the hearing aid
system may be used for analyzing the images. For example, the
processor of the hearing aid system may be configured to receive
various images or characteristics of persons from the database of
server 250 as well as the associated data records for these images
or characteristics, and compare the received images or
characteristics with an image or characteristics of the person
identified in the captured images. Similar to the embodiments
discussed above, the processor of the hearing aid system may use a
computer-based model to compare images and may receive images from
the database that are relevant to location or time for user 100. In
an example embodiment, the computer-based model may include a
neural network such as a convolutional neural network (CNN). In
some embodiments, the determination of whether the at least one
individual is a recognized individual may be based on an output of
a trained neural network supplied with the at least one of the
plurality of images that can be used to analyze one or more images.
In some embodiments, the determination of whether the at least one
individual is a recognized individual may be based on one or more
facial features associated with the at least one individual that
are detected based on analysis of the at least one of the plurality
of images. For example, a computer-based model such as CNN may be
used to analyze images and compare facial features or relations
between facial features of the person identified in the captured
images with facial features or relations therebetween of people
found in images stored in the database of server 250. In some
embodiments, a video of person's facial dynamic movements may be
compared with video data record for various people, obtained from
the database, in order to establish that the person captured in the
video is a recognized individual.
[0375] In various embodiments, the hearing aid system may include
at least one processor programmed to receive an audio signal from
the at least one microphone. In an example embodiment, the at least
one processor may be configured to use a computer-based model to
determine whether the received audio signal is associated with a
recognized individual. For example, the computer-based model may be
a neural network model (e.g., convolutional neural network (CNN)),
and the like. In some cases, the audio signal may include multiple
audio signals from multiple sources (e.g., an audio signal from a
speaker in conversation with user 100, an environmental audio
signal, and the like). In various embodiments, the determination of
whether the at least one individual is a recognized individual may
be based on analysis of the at least one audio signal (e.g., an
audio signal related to user 100 conversing with one or more
speakers) received by the microphone of the hearing aid system. In
an example embodiment, the audio signal may be determined to be
associated with the recognized individual based on a detected look
direction for the user, determined based on a direction associated
with a chin of the user detected in the at least one of the
plurality of images.
[0376] In an example embodiment, a detection in the audio signal of
one or more of predetermined voice characteristics associated with
one or more recognized individuals (e.g., individuals whose
voiceprints and data records are available, for example, in the
database of server 250, or elsewhere) may be used to identify and
recognize one or more speakers. For example, the detection of voice
characteristics of a recognized individual may determine whether
the received audio signal is associated with the recognized
individual. As used herein, the term "voiceprint" may refer to a
set of measurable features (or feature ranges) of a human voice
that uniquely identifies a speaker. In some embodiments, these
parameters may based on the physical configuration of a speaker's
mouth, throat, and additional organs, and/or may be expressed as a
set of sounds related to various syllables pronounced by the
speaker, a set of sounds related to various words pronounced by the
speaker, a modulation or inflection of a voice of the speaker,
cadence of a speech of the speaker, and the like.
[0377] In various embodiments, server 250 may receive images and
audio information (e.g., voiceprints) for various individuals from
a variety of sources. For example, FIG. 32 shows server 250
receiving images 3211 and audio data 3212 from user 100 wearing
apparatus 110. In some cases, images and audio data may be
submitted to server 250 via computing device 120 (e.g., a
smartphone, laptop, tablet, and the like). In some embodiments,
server 250 may be configured to access information (e.g., images,
video, audio data, etc.) available over a social network 3220
(e.g., a Facebook.RTM. page/LinkedIn.RTM. page, email,
Instagram.RTM., and the like) associated either with user 100 or
with one or more individuals identified in images 3211, as shown in
FIG. 32. The information from social network 3220 may include data
related to friends of user 100, to friends of friends of user 100,
and the like. In some embodiments, server 250 may receive
information from individuals that do not use the hearing aid system
(e.g., apparatus 110, as shown in FIG. 32) but who may have a user
profile associated with server 250. For example, user 3230 may be a
relative, co-worker, friend, and the like of user 100, and may have
a user profile associated with server 250. In various embodiments,
user 3230 may take images/video and/or audio data 3231 (e.g., a
selfie as shown in FIG. 32) and upload data 3231 to server 250. In
various embodiments, user 3230 may upload information to server 250
such as an associated data record (e.g., a name of user 3230, a
location, and the like). In an example embodiment, the one or more
processors may be programmed to transmit images 3211 and audio data
3212 to the database relating to encounters with individuals. For
example, the one or more processors may be configured to transmit
images 3211 and audio data 3212 when a speaker for a conversation
is identified and recognized, or/and images 3211 and audio data
3212 related to various conversations with various speakers even if
those speakers are not recognized.
[0378] In various embodiments, the hearing aid system may be
configured to interact with user 100 via visual or audio data. For
example, the hearing aid system may interact with user 100 via a
display using audio signals delivered to user 100 via earpiece
devices, and the like. In an example embodiment, the hearing aid
system may determine whether the at least one individual is a
recognized individual by comparing images captured by the wearable
device with the images and the associated data records stored in
the database of server 250 as described above. When the likelihood
for the image of the person obtained from captured images matching
the data record from the database is above a predetermined
threshold value, the hearing aid system may establish that the
individual captured in the images is a recognized individual.
[0379] In some cases when the likelihood is insufficiently high
(e.g., below the predetermined threshold value) the hearing aid
system may be configured to suggest various possible names for the
person displayed in the captured one or more images. The hearing
aid system may then allow user 100 to select a name that user 100
believes matches best the person displayed in the captured images.
For cases when the hearing aid system includes a display (e.g., a
mobile phone a tablet, device 120 with display 260, as shown in
FIG. 2, or the like), the hearing aid system may cause an image of
the at least one individual to be shown on the display. In some
embodiments, the hearing aid system may present user 100 with one
or more images of individuals associated with one or more suggested
possible names for the person displayed in the captured one or more
images. For example, the hearing aid system may show the one or
more images of individuals on display 260 of device 120.
Additionally, the hearing aid system may inform user 100 about
other information related to individuals associated with one or
more suggested possible names (e.g., estimated/expected locations
of the individuals, occupations of the individuals, etc.) in order
to facilitate user 100 in selecting the name of an individual that
user 100 believes matches best the person displayed in the captured
images.
[0380] In some cases, the display may be included with a housing
common to the wearable camera and the at least one microphone. In
some cases, the wearable camera and the at least one microphone may
be included in the common housing, and the display may be located
elsewhere. In some embodiments, the common housing may further
include a processor. In some cases, the hearing aid system may
include various elements and devices that may not be included in
the common housing. For example, the hearing aid system may include
a second processor that is not included in the common housing. In
some embodiments, the at least one processor is configured to
receive the captured images via a wireless link between a
transmitter in the common housing and receiver in the second
housing. For example, the second housing may be associated with a
paired (e.g., connected wirelessly or wired using any suitable
approach) mobile device. As previously described, the display may
be part of the second housing (e.g., a mobile device such as
smartphone, tablet, laptop, and the like) paired with the hearing
aid system.
[0381] In an example embodiment, the image of the at least one
individual that may be shown on display 260 may be retrieved from a
database stored in memory, (e.g., the database of server 250) that
associates the recognized individuals with corresponding images or
features extracted from images, as described above. In some cases,
the displayed image of the at least one individual may be extracted
(e.g., derived) from the at least one image.
[0382] For cases when at least one individual is determined to be a
recognized individual, the hearing aid system may be configured to
inform user 100 that the individual has been recognized. For
example, for cases when the hearing aid system includes a display,
the hearing aid system may cause an image of the at least one
individual to be shown on display (e.g., display 260 of device
120).
[0383] In some cases, the hearing aid system may be configured to
display information obtained from the data record associated with
the recognized individual such as the individual name, address,
relationship to user 100, and the like. Additionally, or
alternatively, the hearing aid system may be configured to notify
user 100 that the individual has been recognized using audio
signals delivered to user 100 using any suitable means (e.g., using
one or more earpiece devices, a speaker, and the like). For
example, the hearing aid system may inform user 100 via one or more
earpiece devices the information obtained from the data record
associated with the recognized individual, such as the individual
name, address, relationship to user 100, and the like.
Additionally, or alternatively, the hearing aid system may inform
user 100 that the individual has been recognized using any other
suitable approaches (e.g., via a text message, a tactile signal,
and the like).
[0384] The hearing aid system may selectively condition at least
one audio signal that is received from the at least one microphone
and determined to be associated with the recognized individual.
Selective conditioning of an audio signal may involve filtering a
selected audio signal from the audio signal. In some cases,
selective conditioning may include attenuating the audio signal.
Alternatively, selective conditioning may include amplification of
the audio signal. In an example embodiment, the selected audio
signal may correspond to audio related to the conversation of user
100 with another person. In some cases, the audio signal may
include environmental noises (e.g., various background sounds such
as music, sounds/noises from people not participating in
conversation with user 100, and the like), and selected audio
signal may include speech of the person participating in the
conversation with user 100 (referred to as a speaker). In some
embodiments, the selective conditioning may include changing a tone
associated with the at least one audio signal or changing a rate of
speech associated with the at least one audio signal.
[0385] Separating the voice of the speaker from the background
sounds may be performed using any suitable approach, for example,
using a multiplicity of wearable microphones mounted at different
positions on user 100. In some cases, at least one microphone may
be a directional microphone or a microphone array. For example, one
microphone may capture background noise, while another microphone
may capture an audio signal comprising the background noise as well
as the voice of a particular person. The voice may then be obtained
by subtracting the background noise from the combined audio. In
some cases, some of the microphones capable of transmitting audio
to the hearing aid system may be wearable by the person who is
speaking (e.g., a speaker). For example, user 100 may hand the
person who is speaking a removable microphone. In some situations,
there may be two or more persons engaged in a conversation with
user 100, with or without background noise. For example, FIG. 33
shows user 100, wearing an image capturing device 3322 and an audio
capturing device 3323, interacting with a speaker 3302 and a
speaker 3303. In such situations, knowing the identity of at least
one of the speakers, or the number of speakers, may be helpful in
separating the voices.
[0386] The number of speakers may be obtained using, for example, a
speaker estimation algorithm. The algorithm may receive image data
(e.g., an image of speaker 3302 and an image of speaker 3303 as
captured by apparatus 110), and based on the received images,
output whether the conversation includes multiple speakers. Speaker
3302 and speaker 3303 may be identified and recognized by the
hearing aid system by finding a pair of people facing each other.
Multiple images may be captured by the hearing aid system to ensure
that the pair of people continue to face each other over a period
of time. In some embodiments, the hearing aid system may identify
speakers 3302 and 3303 are engaged in conversation with user 100
based on the orientation of their faces, gestures of the speakers
(e.g., nodding by one of the individuals when the second person is
speaking), the timing of the gestures and sounds, etc. In some
embodiments, at least one of the speakers (e.g., speaker 3302) may
be identified by his or her voiceprint. In some embodiments, user
100 may assist the hearing aid in determining the number of
speakers by using a positioning of user 100 head and/or head
gestures. The speaker estimation algorithm may output whether the
conversation includes no speech (e.g., only background noise is
present), a single speaker, or multiple speakers.
[0387] The head positioning and/or head gestures may be used to
determine the number of speakers, and also to determine which audio
signal is associated with which speaker. In various embodiments,
head positioning for user 100 may include orienting a face of user
100 towards a speaker that is talking (e.g., speaker 3303, as shown
in FIG. 33), and maintaining such position for at least a
predetermined duration of time (e.g., for a second, for a few
seconds, or for a duration of a speech of speaker 3303).
[0388] In some embodiments, the hearing aid system may be
configured to use the correlation between head positions of user
100 audio signals received from speaker 3302 and 3303 to establish
the number of speakers for the conversation. Additionally, or
alternatively, head gestures such as nodding, head shaking,
specific head movements, facial movements, etc., may also be used
to indicate to the hearing aid system the number of speakers in the
conversation and which audio signal is associated with which
speaker.
[0389] In some embodiments, attributes of an audio signal (e.g.,
signal from speaker 3302 and speaker 3303) may be used alone or in
combination with image data as well as head positioning data and
head gestures to determine the number of speakers in the
conversation, and which audio signal is associated with which
speaker. For example, if an audio signal includes a first audio
signal having a first distinct tone, cadence, loudness, etc., and a
second audio signal includes a second distinct tone, cadence,
loudness, etc., the hearing aid system may determine that there are
two speakers in the conversation. Furthermore, the hearing aid
system may differentiate between the first and the second audio
signal when these signals are not overlapping (e.g., when speaker
3302 and 3303 are not talking at exactly the same time, which is a
typical situation during a conversation).
[0390] In some embodiments, speech content or speech cadence of one
of the speakers (e.g., the speaker 3302) may be analyzed by the
hearing aid system to differentiate between voices of speaker 3302
and 3303. For example, the hearing aid system may determine, based
on the content of the speech or speech cadence, that speaker 3302
may be awaiting a response from speaker 3303. For example, such a
situation may arise when speaker 3302 asks the speaker 3303 a
question or requests information from speaker 3303. In some
embodiments, some of the keywords may be detected by the hearing
aid system that may indicate that speaker 3302 is awaiting a
response from speaker 3303 (e.g., keywords may include "tell us
about," "what do you think about," etc.). In some cases, the
content or cadence of the speech of speaker 3302 may indicate that
speaker 3302 is planning to continue speaking. For example, speaker
3302 may use phrases such as "I disagree with you because," "the
list includes five items, the first item being," etc.
[0391] In various embodiments, the hearing aid system may be
configured to record or transcribe the conversation between
multiple speakers. The transcription process may be assisted by
captured images by the hearing aid system. For example, the hearing
aid system may identify and recognize speaker 3302 and/or speaker
3303. Speaker 3302 may be facing speaker 3303 (not shown in FIG.
33), and, based on the images captured by image capturing device
3322 of the hearing aid system, the hearing aid system may
determine that speaker 3302 is addressing speaker 3303. The hearing
aid system may be configured to transcribe the conversation between
speaker 3302 and speaker 3303 and to identify the first speech as
belonging to speaker 3302 and the second speech as to belonging to
speaker 3303.
[0392] In various embodiments, a voiceprint of a speaker may be
obtained using an audio signal associated with a speech of the
speaker and stored in the database of server 250 for further
reference. The stored voice data may include one or more
voiceprints that may be obtained from one or more speeches of the
speaker. In an example embodiment, at least one audio signal may be
determined to be associated with the recognized individual based on
one or more predetermined voiceprint characteristics associated
with the recognized individual detected in the at least one audio
signal. The predetermined voiceprint may be stored in association
with a person and one or more images or visual characteristics
thereof, and optionally updated over time, enhanced, or the like.
When the speaker is recognized in one or more images, one or more
voiceprints may be retrieved and used for separating the specific
voice from a mixture of voices. In an example embodiment, the
voiceprint may be stored in the database of server 250 and may be
associated with the data record corresponding to the speaker.
Additionally, the voiceprint may further be associated with one or
more images of the speaker related to the data record.
[0393] Alternatively, for example, if a speaker is not identified,
the speaker's voiceprint may be extracted from an earlier part of
the conversation when only that speaker was engaged in the
conversation. The extraction of the voiceprint may be performed on
segments of the audio for which the number of speaker algorithm
indicates a single speaker. The extracted voiceprint may then be
used later in the conversation for separating the speaker's voice
from other voices. The separated voice can be used for any purpose,
such as transmission over the phone, transmission to a microphone,
transmission to a hearing aid, or the like.
[0394] In some cases, the hearing aid system may be configured to
obtain a first audio sample from the first speaker (e.g., speaker
3302) separated from a second audio sample from the second speaker
(e.g., speaker 3303). The hearing aid system may use the first
audio sample to determine a first voiceprint for speaker 3302 and
the second audio sample to determine a second voiceprint for
speaker 3303. As described above, a speaker communicating with user
100 may be identified using images captured by an apparatus such as
apparatus 110. An individual may be identified as the speaker if
the speaker is located in the center of the user's field of view as
captured by a wearable camera of the hearing aid system. In other
embodiments, the speaker may be identified as a speaker to which
the user's chin, as recognized in one or more images, is
directed.
[0395] The voiceprint extraction may be facilitated by user 100
head position and/or head gestures. For example, at the beginning
of a conversation, user 100 may orient his/her face towards a
speaker that is talking to user 100 as shown, for example, in FIG.
33, by looking at speaker 3303. Similarly, when speaker 3302 is
talking, user 100 may look at speaker 3302 to indicate to the
hearing aid system that an audio signal received by the hearing aid
system is primarily due to the speech of speaker 3302. In an
example embodiment, at the beginning of the conversation, the
hearing aid system may not be configured to separate the specific
voice from a mixture of voices prior to obtaining sufficient data
(e.g., voiceprint related data) to adequately separate voices.
However, once the hearing aid system receives sufficient
information to adequately separate voices, the hearing system may
selectively condition (e.g., abruptly or gradually) the audio
signal related to the conversation of user 100, by separating a
voice of a speaker engaged in a conversation with user 100.
[0396] In various embodiments, a speaker's voiceprint and a
high-quality voiceprint, in particular, may provide for fast and
efficient speaker separation. A high-quality voiceprint for a
speaker may be collected, for example, when the speaker speaks
alone, preferably in a quiet environment. Having a voiceprint of
one or more speakers, a processor of the hearing aid system to
separate an ongoing voice signal almost in real time, e.g., with a
minimal delay, using a sliding time window. The delay may be, for
example, 10 milliseconds, 20 milliseconds, 30 milliseconds, 50
milliseconds, 100 milliseconds, or the like. Different time windows
may be selected, depending on the quality of the voiceprint, on the
quality of the captured audio, the difference in characteristics
between the speaker and other speaker(s), the available processing
resources, the required separation quality, or the like.
[0397] A voiceprint extraction may be performed by extracting
spectral features, also referred to as spectral attributes,
spectral envelope, or spectrogram from clean audio of a single
speaker. The clean audio may include a short sample (e.g., one
second long, two seconds long, and the like) of the voice of a
single speaker isolated from any other sounds such as background
noises or other voices. The clean audio may be input into a
computer-based model such as a pre-trained neural network, which
outputs a signature of the speaker's voice based on the extracted
features. Such signature of the speaker's voice may include the
same information as a voiceprint for the speaker. Additionally, or
alternatively, the signature of the speaker's voice may include
audio information that may be used to obtain the voiceprint for the
speaker. In some cases, the signature of the speaker's voice may
include audio information that can be used to obtain at least some
of the data needed to determine the voiceprint for the speaker.
[0398] The output signature may be a vector of numbers. For
example, for each audio sample submitted to a computer-based model
(e.g., a trained neural network), the computer-based model may
output a set of numbers forming a vector. Any suitable
computer-based model may be used to process the audio data captured
by one or more microphones of the hearing aid system to return an
output signature. In an example embodiment, the computer-based
model may detect and output various statistical characteristics of
the captured audio such as average loudness or average pitch of the
audio, spectral frequencies of the audio, variation in the
loudness, or the pitch of the audio, rhythm pattern of the audio,
and the like. Such parameters may be used to form an output
signature comprising a set of numbers forming a vector.
[0399] The output signature may be a first vector representing the
speaker's voice, such that the distance between the first vector
and another vector (i.e., another output signature) extracted from
the voice of the same speaker is typically smaller than the
distance between the output signature of the speaker's voice and
the output signature extracted from a voice of another speaker. In
some embodiments, output signature of the speaker's voice may be a
voiceprint for the speaker and may include a sound spectrogram that
may be a graph that shows a sound's frequency on the vertical axis
and time on the horizontal axis. Different speech sounds may create
different shapes within the graph. The voiceprint may be
represented visually and may include colors or shades of grey to
represent the acoustical qualities of a sound of the speaker's
voice.
[0400] FIG. 34A shows a flowchart of a process 3400 for separating
voices in an audio signal. At step 3451, an audio signal 3401 may
be received by a hearing aid system. The hearing aid system may
include a computer-based model 3403 for separating an audio signal
that corresponds to a voice of a speaker from the background sounds
using any suitable approaches described above. In some cases, the
hearing aid system may record a "room tone" prior to the beginning
of the conversation, where room tone may refer to the natural noise
of the environment of user 100. The audio signature corresponding
to room tone may be used to filter out the background noise from an
audio signal containing conversational sounds.
[0401] Model 3403 may output a voice audio signal 3404 at step
3452. At step 3453, signal 3404 may be received by a voice model
3410, and at step 3454 the voice model may output voiceprint 3411
for the speaker's voice. Model 3410 may use any suitable approach
describing above for obtaining voiceprint 3411 from the speaker's
voice, such as extracting a spectrogram from the speaker's voice,
extracting statistical audio characteristics, and the like. At step
3455 of process 3400, a computer-based model 3430 may receive
voiceprint 3411 and an audio signal 3421 that may include
background sounds, and/or one or more voices of one or more
individuals.
[0402] In various embodiments, computer-based model 3430 may be a
neural network. At step 3456, model 3430 may receive the noisy
audio signal 3421 and the speaker's signature or voiceprint 3411,
and, at step 3457A or/and 3457B, output audio signal related to a
voice 3431A and/or a voice 3431B. It should be noted, that filtered
(i.e., separated) voice 3431A and/or 3431B may be used to prepare
an additional voiceprint (or/and output signature) for the speaker
that can be used by computer-based model 3430. In some embodiments,
more than one voiceprint (e.g., voiceprint 3411) may be used as an
input for model 3430 at step 3455. In some embodiments, multiple
voiceprints may correspond to the same individual, and in other
embodiments, some of the voiceprints may correspond to a first
person (e.g., speaker 3303, as shown in FIG. 33) and other
voiceprints may correspond to a second person (e.g., speaker 3302,
as shown in FIG. 33).
[0403] FIG. 34B shows an illustrative process 3470 for separating
voice signal from an audio signal using a video signal. At step
3461, model 3445 may receive data 3443 related to a conversation of
user 100 with speakers 3302 and 3303. Data 3443 may include a video
signal 3441 and an audio signal 3421. Video signal 3441 may
indicate whether a speaker (e.g., speaker 3303) is talking or
silent. For example, video signal 3441 may show the lips movement
of speaker 3303. Audio signal 3421 may include a background sound
as well as voices of speaker 3302 and 3303 that may or may not
overlap. For example, the voice of speaker 3302 may overlap briefly
with the voice of speaker 3303. Model 3445 may identify, and
separate voices for speaker 3302 and 3303 by synchronizing the lips
movement of speaker 3303 (or speaker 3302) with words/sounds
identified in audio signal 3421. In some embodiments, the hearing
aid system may be configured to collect video and audio data for
both of the speakers. For example, the hearing aid system may be
configured to detect lip movements of speaker 3302 and speaker 3303
during the conversation. At step 3457A or/and 3457B, model 3445 may
output audio signal related to a voice 3431 and/or a voice
3432.
[0404] In various embodiments, the selectively conditioned audio
signal (e.g., voice 3431A or voice 3431B) processed by the hearing
aid system as described in process 3400 or process 3470 may be
transmitted to an interface device (e.g., an earpiece, headphones,
a speaker, a display, a vibrational or tactile device, etc.) for
delivering the audio signal to user 100. In various embodiments,
the interface device may be part of the hearing aid system. In an
example embodiment, the interface device may transmit to user 100
an audio signal (e.g., a signal transmitted to user 100 via an
earpiece), a visual signal (e.g., a text written on a screen or a
video of a person communicating with user 100 via a silent
language), a vibration signal, a tactile signal (e.g., tactile
letters used by visually impaired people for reading), an electric
signal, and the like. In an example embodiment, the interface
device may include a hearing interface device configured to provide
sound to an ear of the user. In an example embodiment, the hearing
interface device may include a speaker associated with an earpiece
or a bone conduction microphone.
[0405] In some embodiments, the hearing interface device of the
hearing aid system may transmit to user 100 an audio signal
corresponding to a speech of a speaker as it is extracted from an
audio signal captured by one or more microphones of the hearing aid
system. Additionally, or alternatively, the hearing aid system may
be configured to modify one or more parameters of the audio signal
corresponding to the speech of the speaker. For example, the
hearing aid system may modify the pitch, loudness, cadence, etc.,
of the audio signal prior to providing the signal to user 100 via
the hearing interface device. In some embodiments, the hearing aid
system may be configured to transcribe the speech of the speaker,
modify the transcribed speech, and read the transcribed speech
using a text-to-speech natural voice artificial intelligence
reader. In some embodiments, when multiple voices are detected
(e.g., when voices 3431A and 3431B overlap), the hearing aid system
may be configured to time shift one voice relative to another to
reduce the overlap. Alternatively, the hearing aid system may be
configured to modify one or more characteristics of one of the
voices (e.g., voice 3431A) to further differentiate it from voice
3431B. For example, the tone, cadence, etc. of voice 3431A may be
modified to differentiate voice 3431A from voice 3431B.
[0406] In some embodiments, when multiple microphones are present
(e.g., two microphones are present), a delay of the audio signal
measured between the two microphones may be used to determine the
directional characteristics of the audio signal related to the
speaker's voice. In an example embodiment, user 100 may have a left
microphone positioned next to a left ear and a right microphone
positioned next to a right ear. A left speaker, engaged in
conversation with user 100, may be positioned slightly to the left
of user 100, and audio signals from the left speaker may arrive
first in the left microphone, and second, to the right microphone,
resulting in a phase shift between audio signals received by the
two microphones. The phase shift may be used to distinguish from
other signals that may not have a well-defined phase shift, or
which may have a different phase shift. For example, if another
speaker is present (e.g., a right speaker positioned slightly to
the right of user 100), audio signals from the right speaker may
have a different phase shift compared to the phase shift for the
left speaker. For example, the audio signals from the right speaker
may arrive first to the right microphone of user 100, and after
that, to the left microphone of user 100, resulting in a phase
shift that has an opposite sign from the phase shift for the left
speaker. In various embodiments, user 100 may be able to move
his/her head to further differentiate between left and right
speaker using the left and the right microphone.
[0407] In some cases, an audio signal from different speakers
cannot be separated by the hearing aid system due to, for example,
an unavailable voiceprint for the one or more speakers, a
low-quality voiceprint for the one or more speaker, two or more
voices of the different speakers being similar to each other, or
the like. In such cases, when it is determined that there are two
or more voices present in a conversation, the output signal may be
silenced. This feature may help user 100 adapt in a noisy
environment. For example, such a feature may prevent user 100 from
hearing loud and unpleasant noise while not being able to
understand what is being said. Thus, silencing the output signal,
as described above, may not reduce user 100 understanding of the
conversation, but may reduce the environmental noise and thus
improve user 100 with a comfort level during the conversation. In
various embodiments, the silencing of the output signal can be
partial (also referred to as suppression of the output signal).
[0408] FIG. 35A shows an illustrative process 3500 for transmitting
a voice separated from an audio signal to a device, such as
earpiece of the hearing aid system. At step 3504, one or more
images captured by the apparatus described above may be received by
a processor of a hearing aid system or by a processor of server
250. In some embodiments, the images may be captured substantially
in line with the user's line of sight, such that a speaker
communicating with user 100 is at or near the center of the image.
At step 3508, the speaker may be identified in the captured images
using any suitable approaches as described above. At step 3512, the
speaker may be recognized. The speaker identification may relate to
locating a person within an image, while recognition of the speaker
may relate to recognizing the identified person as being a specific
known person. The person may be recognized if previously captured
by the device, and his or her name or another detail were provided
by a user or in any other manner. The recognition of the speaker
may be done using any suitable approaches described above. At step
3514, an image of the recognized individual may be shown on a
display associated with the hearing aid system. For example, an
image of the recognized individual may be retrieved from the
database and displayed on a mobile device paired with the hearing
aid system.
[0409] At step 3516, an audio signal may be received by a
microphone of the hearing aid system (e.g., an audio signal of user
100 communicating with another individual). The audio signal may be
further analyzed by a processor of the hearing aid system. In an
example embodiment, the processor may be configured to determine if
the captured audio signal corresponds to a recognized individual.
For example, the processor may be configured to retrieve the
recognized individual voiceprint, from a storage device, based to
the person's identity. For example, the voiceprint may be retrieved
from the database of server 250. Additionally, or alternatively,
the voiceprint may be obtained by analyzing a speech of the speaker
during a conversation that does not contain audio signals from
other speakers, and/or does not contain a high volume of
environmental noises. For example, a conversation may be first
conducted in a quiet environment (e.g., a car prior to arriving at
an event) followed by a conversation at a restaurant (e.g., a noisy
environment).
[0410] At step 3520, the received audio signal may be selectively
conditioned by the processor. Additionally, or alternatively, the
received audio signal may be transmitted to server 250 and
selectively conditioned by one of the processors of server 250. In
an example embodiment, selectively conditioning may include
separating voices of one or more speakers from the audio signal.
Separation may be performed using any of the approaches described
above. In some embodiments, if the speaker is recognized at step
3512, and the voiceprint of the speaker is obtained at step 3602,
the received audio may be separated by extracting only the voice of
the particular speaker rather than all voices participating in the
conversation or background voices (e.g., voices that do not
directly engage user 100). Selective conditioning may use any of
the suitable approaches discussed above.
[0411] At step 3524, the speaker's voice may be provided to the
hearing aid system of user 100 to help user 100 focusing on the
conversation with the speaker while reducing disturbances from the
environmental noises and/or from other background voices. As
previously described, the audio signal related to the voice of the
speaker may be altered by the hearing aid system (e.g., the voice
may be amplified, and/or otherwise modified, for example, by
changing the tone of the voice, using noise cancellation or other
approaches).
[0412] In various embodiments, the audio processing may be combined
with an image processing technique, for example by identifying, and
in some case recognizing a speaking person, synchronizing received
audio with a motion of lips of a speaker in communication with user
100 and/or lip reading based on the motion of the speaker's lips
(e.g., the sound "ba" may be uttered by a person who opened his/her
mouth). In another example, if no person in an environment of user
100 is speaking, then background noise may be detected and
canceled.
[0413] In some embodiments, the audio signal received by user 100
may arrive to user 100 via different channels. For example, user
100 may participate in a phone audio/video conversation with a
speaker, and background noises may be due to an environment of user
100. In such cases, the background noises may be suppressed.
[0414] In various embodiments, the hearing aid system may be
operated by a battery. In order to prolong the functioning of the
hearing aid system, various approaches may be used to reduce the
power consumption of the hearing aid system. For example, the
hearing aid system may optimize a rate of capturing video frames,
reduce the resolution of captured images, optimize compression of
the captured images and/or optimize compression/quality of a
captured audio signal. Other steps for reducing power consumption
by the hearing aid system may include optimizing the process of
data transfer from the hearing aid system to server 250. For
example, the hearing aid system may be configured to transfer data
to server 250 periodically, with time intervals between data
transfers increasing when a reduction in power consumption by the
hearing aid system occurs.
[0415] As described, in various embodiments, the voice audio signal
corresponding to a speech of a speaker may be manipulated prior to
transmitting it to user 100. For example, if a rate of the speech
exceeds a predetermined value, the speech may be slowed and
transmitted to the hearing aid system at a lower rate. The lower
rate may be compensated during breathing or other pauses, so as not
to accumulate delay. In further embodiments, slang or inappropriate
words may be replaced. Such features may be useful, for example, in
helping older people communicate with younger ones, for example,
their grandchildren. In some embodiments, slower speech may be
accelerated, which may help prevent boredom or allow a user to more
rapidly listen to audio.
[0416] In some embodiments, the database may be configured to
establish a timeline for various encounters with various speakers
and track the encounters with different individuals
chronologically. In some cases, based on input received from user
100, the one or more processors of the hearing aid system may be
configured to forego transmitting to the database information
related to encounters with one or more individuals identified in
the plurality of images, thus, preventing the storage of such
information. For example, the information related to the encounter
may be ignored (i.e., not stored in the database of server 250) if
user 100 believes the encounter is not important, or if she/he
prefers the information not to be available for access/inspection
later by a third party and/or by user 100. In some cases, to
prevent access to the information stored in the database, the
information may be password protected.
[0417] In some instances, the one or more processors of the hearing
aid system may be configured to forego transmitting to the database
information related to encounters with one or more individuals
determined to be associated with a predetermined group of
individuals. For example, a group of individuals may be identified
by identifying members of the group or by identifying attributes of
individuals in the group (e.g., all the individuals that wear a
uniform). The attributes of the individuals may be identified by
user 100 and entered using a user interface for the hearing aid
system. In some cases, the attributes of the individuals may be
inferred from the images captured by the hearing aid system. In
example embodiments, the one or more predetermined groups of
individuals may include office workers, service personnel, or
various individuals with whom user 100 does not participate in
vocal interactions. In some embodiments, the predetermined group
may include individuals who do not participate in a conversation
with user 100, and in some embodiments, the predetermined group may
include individuals who do not participate in a conversation with
individuals that participate in a conversation with user 100. The
information related to the encounter with one or more individuals
determined to be associated with a predetermined group of
individuals may be ignored (i.e., not stored in the database of
server 250) if user 100 believes the encounter is not important, or
if she/he prefers the information not to be available for
access/inspection later by a third party and/or by user 100. In
some cases, to prevent access to the information stored in the
database, the information may be password protected.
[0418] FIG. 35B is an illustrative process 3550 for recording an
encounter in a timeline. At step 3551 of process 3550, the hearing
aid system may be configured to capture the encounter with an
individual for user 100 using any suitable approaches described
above. For example, the hearing aid system may be configured to
capture the encounter by directing a camera and a microphone of the
hearing aid system to capture the image/video data and audio data
related to the encounter. At step 3553, the hearing aid system may
obtain input from user 100 whether the encounter should be ignored.
For example, the hearing aid system may obtain an audio signal from
user 100 to ignore the encounter. Additionally, or alternatively,
the hearing aid system may obtain user 100 input regarding ignoring
the encounter via a touch screen (e.g., a touch screen of a mobile
device paired with the hearing aid system). If the encounter is
determined not to be ignored (step 3553, No), the encounter may be
recorded in a timeline at step 3555. Recordation of the encounter
may allow user 100 to retrieve information associated with the
encounter by specifying some identifying characteristics of the
encounter, such as date and time of the encounter, the nature of
the encounter, the speaker identified and recognized in the
encounter, the topic of conversation, etc. If the encounter is
determined to be ignored (step 3553, Yes), the encounter may not be
recorded, and process 3550 may be terminated. In some embodiments,
if the encounter is determined to be ignored (step 3553, Yes), the
encounter may be recorded for a predefined period of time before it
is deleted from the timeline. Such temporal recordation of the
encounter may allow user 100 to change his/her mind regarding
ignoring the encounter.
[0419] Selectively Conditioning Audio Signals Including Overlapping
Voices
[0420] In some embodiments, a hearing aid system may include at
least one processor programmed to receive audio signals from the at
least one microphone. The processor may be configured to detect,
based on analysis of the audio signals, a first audio signal
associated with a first time period, wherein the first audio signal
is representative of a voice of a single individual. In addition,
the processor may be configured to detect, based on analysis of the
audio signals, a second audio signal associated with a second time
period, wherein the second time period is different from the first
time period, and wherein the second audio signal is representative
of overlapping voices of two or more individuals.
[0421] The audio signal corresponding to the overlapping voices may
include at least two overlapping voices, and in some cases, may
include more than two voices. In some cases, some of the
overlapping voices may be in close proximity to user 100, and have
a high amplitude, while other overlapping voices may be further
away from user 100 and have a lower amplitude. In some cases a
voice with a high amplitude may overlap a voice with a lower
amplitude. In various embodiments, a first and a second voice
overlap when a sound associated with the first voice is emitted
during a first time interval, a sound associated with the second
voice is emitted during a second time, and the first and the second
time intervals overlap. In some cases, the first and the second
time windows may overlap partially. For example, a portion of the
first time window may overlap with a portion of the second time
window. It should be noted that, the duration of the first time
window may be shorter or longer than a duration of the second time
window.
[0422] When the audio signal contains more than two voices, more
than two voices may overlap. For example, when audio signal
includes a first, a second, and a third voice, the first and the
second voice may overlap, the second and the third voice may
overlap, the third and the first voice may overlap, and, in some
cases, all three voices may overlap.
[0423] In an example embodiment, the processor may selectively
condition the first audio signal and the second audio signal,
wherein the selective conditioning of the first audio signal may be
different in at least one respect relative the selective
conditioning of the second audio signal. For example, selectively
conditioning the first audio signal may include removing the
background sound and separating the voice of the individual
detected in the first audio signal. Additionally, or alternatively,
selective conditioning the first audio signal may include
amplification of the signal, changing a tone of the signal, or
changing a rate of speech associated with the signal. In an example
embodiment, selectively conditioning the second audio signal may
include amplification of the second audio signal. In some cases,
when both the first and the second audio signals are amplified, the
amplification level associated with the second audio signal may be
less than the amplification level associated with the first audio
signal. In some embodiments, the selective conditioning of the
second audio signal includes attenuation of the signal. For
example, the signal may be completely or partially attenuated. In
some cases, some of the frequencies of the second audio signal may
be attenuated. Additionally, or alternatively, some of the portions
of the second audio signal may be attenuated while other portions
may be unchanged or amplified. In some cases, the amplitude of the
second audio signal may be attenuated in a time-dependent way, and
in some cases, an amplitude of a set of frequencies of the second
audio signal may be attenuated in a time-dependent way. In some
cases, the selective conditioning of the second audio signal
includes foregoing transmission of the second audio signal to the
hearing interface device configured to provide sound to an ear of
the user. For example, the second audio signal may not be
transmitted to the hearing interface device when voices in the
second signal are not clearly discernible and may cause confusion
to user 100.
[0424] In various embodiments, at least one processor of the
hearing aid system may be programmed to analyze the plurality of
images captured by a wearable camera (e.g., image capturing device
3322) and identify in at least one of the plurality of images
representation of a single individual associated with the first
audio signal. For example, the processor may be programmed to
capture a video data related to facial expressions of the
individual and analyze video frames by evaluating the correlation
between facial expressions and sounds detected in the first audio
signal. In some cases, the analysis may identify correlations
between particular facial expressions and particular sounds or
sound fluctuations. For example, a facial expression related to a
particular lip movement may be associated with a sound or word that
may have been said during a conversation captured in the first
audio signal. In some embodiments, the analysis of the plurality of
images is performed by a computer-based model such as a trained
neural network. For example, the trained neural network may be
trained to receive an image and/or video data related to facial
expressions of an individual and predict a sound associated with
the received image and/or video data. For example, the trained
neural network may be trained to receive an image and/or video data
related to facial expressions of an individual and a sound, and
output whether the facial expressions correspond to the sound. In
some embodiments, other factors, such as gestures of the
individual, the position of the individual, the orientation of the
individual's face, etc., may be identified in the one or more
images captured by a wearable camera. These factors may be used
alone or in combination with facial expressions of the individual
to determine if the individual is associated with the first audio
signal.
[0425] In some embodiments, at least one audio signal (e.g., the
first audio signal) is determined to be associated with the
recognized individual based on lip movements of the user, as
detected based on analysis of the plurality of images. For example,
the first audio signal may be determined to be associated with the
recognized individual based on a determination of whether the
detected lip movements are consistent with a voice signal
associated with the at least one audio signal.
[0426] In various embodiments, at least one processor of the
hearing aid system is configured to transmit the conditioned first
audio signal to a hearing interface device configured to provide
sound to an ear of user 100 using any suitable approaches discussed
above. In some embodiments, the processor may also be configured to
transmit conditioned second audio signal to the hearing interface
device configured to provide sound to an ear of user 100 using any
suitable approaches discussed above.
[0427] FIG. 36A shows an illustrative process 3600 describing
exemplary steps for transmitting a voice audio data to user 100
from a speaker in communication with user 100. At step 3602 the
hearing aid system may receive captured audio. Step 3602 of process
3600 may be similar to step 3516 of process 3500. At step 3603, the
hearing aid system may determine if the received audio includes a
speech by a single speaker. Such determination may be done using
any suitable approaches described above (e.g., using multiple
microphones that may determine a phase shift corresponding to an
audio signal for the speech of the speaker, using a computer-based
model that can take as an input a voiceprint of the speaker, an
audio analysis algorithm that can evaluate a number of speakers,
and the like). If the hearing aid system determines that the
received audio includes a speech from a single speaker (step 3603,
Yes), at step 3605 a voiceprint of the speaker may be extracted
from the audio, and optionally used for enhancing a previously
available voiceprint. In some embodiments, the voiceprint may be
associated with the speaker that is recognized by the hearing aid
system, and in some embodiments, the voiceprint may be extracted
for the speaker when the speaker is not recognized. In some
embodiments, if a voiceprint exists for the speaker, then a new
voiceprint may not be extracted from the received audio data. At
step 3607, the speaker's voice may be transmitted to the hearing
interface device of the hearing aid system and delivered to user
100 as an audio signal as described above. In some embodiments, the
voice may be transmitted to user 100 via a visual interface,
tactile interface, and the like.
[0428] If at step 3603 the hearing aid system determines that the
received audio signal includes voices of multiple speakers (step
3603, No), process 3600 may be configured to follow step 3604 and
determine if a voiceprint for at least one speaker is available. In
various embodiments, if one or more speaker is recognized by the
hearing aid system, the hearing aid system may access data
associated with the one or more speaker and determine if the
related voiceprint is available. For example, the hearing aid
system may access the speaker data record from the database of
server 250 to determine if the voiceprint is available. Step 3604
may use a trained model to determine whether or not an audio signal
comprises speech associated with a particular voiceprint, or
provide a probability that the audio signal comprises speech
associated with the particular voiceprint.
[0429] In some embodiments, once a speaker in communication with
user 100 is recognized and her/his voice audio data is separated
from an audio signal for the conversation and transmitted to user
100, the voice audio data may be transmitted as long as the speaker
is continuously speaking, even if/when other voices, whether
recognized or not are captured. Such an approach may be used to
allow user 100 to continuously listen to the speaker. Short breaks
in speaking, for example, breathing breaks or pauses while
searching for a word may still be considered as a continuous
speech. In some embodiments, pauses up to a predetermined length
may be considered as part of continuous speech, while longer
periods may be considered as the end of a speech by the speaker,
such that other speakers can be detected or amplified.
[0430] If a voiceprint is available, (step 3604, Yes) the speaker's
voice may be separated from the audio data and transmitted to user
100 at step 3606. If no voiceprint is available, and/or if the
separation of the speaker's voice from the audio data is not
successful, (step 3604, No) the hearing aid system may silence the
output at step 3601. The output may be silenced using any of the
approaches described above. In some embodiments, completely
silencing the rest of the voices may create an uneasy and out of
context feel, for example, when speaking to a person in a
restaurant and seeing the waiter approaching and talking but not
hearing anything. Therefore, providing a low but positive
amplification for the other sound, for example, 10%, 20%, or any
other suitable degree of the volume may feel more natural for user
100. Similarly, if no voice is recognized by the hearing aid
system, instead of silencing everything, the loudness of the
environmental noises can be reduced to a predetermined level. In
such circumstances, the audio related to the environmental sounds
may be transmitted at a low volume (e.g., 10% of the original
volume) for a more natural feeling, enabling user 100, for example,
to hear some background noise at a restaurant. The loudness level
selected by the hearing aid system may be set by a user or
predetermined, depending on an environmental situation, location of
user 100, time of the day, and the like.
[0431] The hearing aid system may be configured to process
environmental sounds and determine if some of the sounds may or may
not be silenced or suppressed. For example, sounds that may be
important and related to user 100 may not be filtered, suppressed,
or silenced. For example, emergency sounds, such as a fire alarm, a
siren, sounds of screams, sounds of crying kids, etc. may not be
silenced, modified, or suppressed.
[0432] In some embodiments, some sounds (whether predetermined,
recognized or not) may be amplified and transmitted at a delay. For
example, in an airport when there is an announcement about a
flight, the device may realize that this is an important
announcement only after the flight number is mentioned. Then it can
play the whole announcement even though the sound of the
announcement may not relate to the voice of a speaker engaged in
conversation with user 100.
[0433] In various embodiments, the voice separation and
amplification process may be time-dependent and may depend on a
content of audio captured by the microphones of the hearing aid
system as well as environmental factors may be determined, for
example, by analyzing the images captured by the hearing aid
system. In some embodiments, the hearing aid system may collect
audio data during a predetermined sliding time window and then
separate voice audio data within such a window. For a short time
window (e.g., a millisecond, few milliseconds, a second, few
seconds, etc.) user 100 may experience only a short delay, between
captured audio and transmitted voice data to user 100. In an
example embodiment, the time window may be less than a second, for
example, 10 milliseconds, 20 milliseconds, 50 milliseconds, 100
milliseconds, or the like.
[0434] In some embodiments, the hearing aid system may enable user
100 to select an audio voice signal from certain individuals to
take precedence over other audio voice signals. In various
embodiments, the voiceprints for these individuals may be extracted
and stored. If one of the indicated speakers is recognized within
an image captured by the hearing aid system, or if their voices are
recognized within a captured audio, these selected voices may be
amplified over voices of other speakers or other sounds, such as
sounds from a television or speaker. For example, the voice of a
parent, a spouse, a child, a grandchild, a great-grandchild, or
other family members may be identified and amplified for user 100,
enabling user 100 to identify these voices over other voices or
other environmental sounds.
[0435] In some embodiments, the hearing aid system may interact
with various other devices when audio signals related to
voices/sounds of recognized individuals (including a voice of user
100) are captured by the hearing aid system. For example, the
hearing aid system may interact with smart-home devices that may
affect the environment of user 100. For example, the hearing aid
system may interact with smart-home devices to turn the light
on/off, to make special sounds, to turn the television on/off, and
the like.
[0436] Audio sounds not related to voices may be separated from
other audio signals. For example, such audio sounds may include
sounds of a dog barking or howling, the sound of a baby crying or
making sounds, sounds of broken glass, sounds of dropped objects,
sounds of creaking/opening/closing doors, doorbell sounds, and the
like. These sounds may be amplified relative to other environmental
sounds that may be less important (e.g., the sound of a radio
station).
[0437] The amplified voices may be provided to user 100 via a
hearing aid, while other sounds may be silenced or suppressed. User
100 may configure the hearing aid system to silence or suppress
voices of selected individuals (the selected individuals may be
selected, for example, by user 100 via an interface device for the
hearing aid system). For example, the selected individuals may be
recognized in one or more images captured by the hearing aid
system, or these individuals may be recognized within captured
audio due to their voiceprint. As described above, in addition to,
or instead of amplifying an audio signal related to the selected
voices, the audio signal of the voices may be enhanced, for
example, the tone may be changed, or other modifications may be
made.
[0438] In some embodiments, the hearing aid system may create a
"hierarchy" of voices for various speakers, wherein the hierarchy
may or may not be time or situation-dependent. For example, during
a lecture, user 100 may want the lecturer's voice to be amplified,
even when the user is looking at another person. In an example
embodiment, when user 100 is in a meeting, user 100 may want to
receive a speech from a selected person or a group of people (e.g.,
from a supervisor of user 100). In some embodiments, user 100 may
separate various voices within the captured audio data but amplify
only one selected voice. In various embodiments, other voices may
be recorded or/and transcribed. In some cases, user 100 may discard
the voices of some individuals that are deemed unimportant.
[0439] In various embodiments, the hearing aid system may be
apparatus 110 that may include a processor and a memory as
described above. The hearing aid system may include software
applications that may contain software modules stored in a memory
3650 of the hearing aid system as schematically shown in FIG. 36B.
The software modules may include a speaker identification module
3654, a speaker separation module 3658, a speaker and speech
matching module 3662, a voiceprint handling module 3666, and a user
interface 3670.
[0440] Modules 3654, 3658, 3662, 3666, and 3670 may contain
software instructions for execution by at least one processing
device, e.g., processor 210, included with a wearable apparatus. In
some embodiments, any one or more of the modules may facilitate
processing one or more images from an image sensor, e.g., image
sensor 220 and audio sensor to generate a set of instructions to
assist user 100 in improving user 100 hearing voice of one or more
speakers.
[0441] Speaker identification module 3654 can be used in
identifying one or more speakers in an image captured by the
apparatus, such that a speaker communicating with user 100 may be
identified. Speaker identification module 3654 can identify the
speaker by his/her location in the captured images that may display
a field of view of user 100. For example, if the speaker is in the
center of the image, the speaker may be in the center of the field
of view. In some embodiment, the direction of user 100 head may be
used to identify a speaker. For example, the hearing aid system may
be configured to capture an image in a direction normal to a face
of user 100 (e.g., normal to a chin of user 100) to discern a
speaker in the captured image. In some embodiments, module 3654 may
identify the data record for the speaker, as previously discussed.
In an example embodiment, module 3654 may associate at least one
audio signal with the identified and recognized individual based on
a detected look direction for the user, determined based on a
direction associated with a chin of the user detected in the at
least one of the plurality of images.
[0442] Voiceprint handling module 3666 may be used to generate,
store, or retrieve a voiceprint, using, for example, wavelet
transform or any other attributes of the voice of one or more
persons. Voiceprint handling module 3666 may use any suitable
algorithm to determine whether an audio signal comprises speech, as
well as to determine whether there is one speaker or multiple
speakers participating in a conversation. A voiceprint may then be
extracted from single speech audio using a neural network as
described above. Information obtained using voiceprint handling
module 3666 may be transmitted to speaker identification module
3654, and module 3654 may associate the identified speaker with the
voiceprint.
[0443] In various embodiments, speaker separation module 3658 may
receive noisy audio captured by the device and voiceprints of one
or more speakers, and separate one or more voices for one or more
speakers using any of the methods described above. In some
embodiments, for example when no voiceprint is available for the
speaker, matching the voice with a specific speaker may be
performed in accordance with the captured images, for example by
matching identified words with the lip movement of the speaker, by
matching speaking and silent periods, or the like.
[0444] In some embodiments, when the identity of the speaker is
determined from the captured images, speaker and speech matching
module 3662 may be used to match the identity of the speaker with
the audio signal corresponding to the voice of the speaker detected
using speaker separation module 3658. In some embodiments, when
speaker identity is not established, an image of the speaker may be
used by module 3662 to correspond with the audio signal of the
voice of the speaker.
[0445] In various embodiments, the hearing aid system may include
user interface 3670 to allow user 100 to change performance
characteristics of the hearing aid system. In some embodiments, the
user interface 3670 may include an interface for receiving a
visual, audio, tactile, or any other suitable signal from user 100.
For example, the interface may include a display that may be part
of a mobile device (e.g., a smartphone, laptop, tablet, etc.) In an
example embodiment, the interface may include a touch screen, a
graphical user interface (GUI) having GUI elements that may be
manipulated by user gestures, or by appropriate physical or virtual
(i.e., on screen) devices (e.g., keyboard, mouse, etc.). In some
embodiments, interface 3670 may be an audio interface capable of
receiving user 100 audio inputs (e.g., user 100 voice inputs) for
adjusting one or more parameters of the hearing aid system. For
example, user 100 may adjust the loudness of the audio signal
produced by the hearing aid system using audio voice inputs, the
pitch of the audio signal produced by the hearing aid system, tempo
of the audio signal, and the like. In some embodiment, user
interface 3670 may be configured to assist user 100 in identifying
the data record for a speaker in conversation with user 100 and for
facilitating separation of the voice of the speaker from the audio
data captured by microphones of the hearing aid system. For
example, interface 3670 may prompt user 100 to select a name for
the speaker from a list of available names, to display an image of
the speaker, to select an audio stream corresponding to the voice
of the speaker, and the like.
[0446] FIG. 37A shows an illustrative process 3700 of transmitting
a conditioned audio signal to a device (e.g., an earpiece of the
hearing aid system). At step 3504, one or more images of a speaker
engaged in a conversation with user 100 may be captured by the
hearing aid system. Step 3504 of process 3700 may be the same as
step 3504 of process 3500. At step 3508, the speaker may be
identified as described above. In various embodiments, step 3508 of
process 3700 may be the same as step 3508 of process 3500. At step
3701, the hearing aid system may determine if the identified
speaker is recognized using any of the suitable approaches
described above. If the speaker is recognized (step 3701, Yes) the
image of the recognized person may be displayed in step 3703. If
the speaker is not recognized (step 3701, No) process 3700 may be
terminated. In some embodiments, if the speaker is not recognized,
the audio signal associated with the voice of the speaker may be
silenced or suppressed as described above.
[0447] At step 3516, a processor of the hearing aid system may
receive audio data related to user 100 conversing with one or more
speakers. In various embodiments, step 3516 of process 3700 may be
the same as step 3602 of process 3500. At step 3705, the processor
of the hearing aid system may selectively condition the received
audio data using any suitable approaches described above. For
example, the processor may selectively condition the received audio
data by analyzing the data and separating one or more voice audio
data related to the one or more speakers from the received audio
data. In some embodiments, selectively conditioning the audio data
may include removing audio background noise data as described
above.
[0448] At step 3707, the conditioned audio signal may be provided,
for example transmitted, to the hearing aid system of user 100. For
example, the conditioned audio signal may be provided to a hearing
interface device (e.g., earpiece, headphones, speaker, etc.). Step
3707 may be similar to step 3524 of process 3500.
[0449] FIG. 37B shows an illustrative process 3760 of transmitting
a conditioned audio signal to a device (e.g., an earpiece of the
hearing aid system). At step 3761, a processor of the hearing aid
system may be programmed to receive an audio signal from the at
least one microphone. In various embodiments, step 3761 may be the
same as step 3602 of process 3500.
[0450] At step 3762, a processor of the hearing aid system may
determine if the audio signal is associated with or comprises
speech by a recognized individual. The processor may make the
determination using any of the suitable approaches described above,
such as comparing the audio signal from the individual with a
voiceprint of a person whose image is being recognized, using a
trained engine, or the like.
[0451] At step 3763, an image of the individual may be displayed on
a screen of a computing device (e.g., a mobile device) available
for user 100. In various embodiments, step 3763 may be the same as
step 3514 of process 3500. At step 3764, the processor of the
hearing aid system may selectively condition the received audio
signal. In various embodiments, step 3764 may be the same as step
3520 of process 3500. In some embodiments, the audio signal may be
transmitted to server 250, and a processor of server 250 may
selectively condition the audio signal. Selective conditioning of
the received audio signal may be achieved using any suitable
approaches described above. For example, the processor of the
hearing aid system may selectively condition the received audio
signal by analyzing the signal and separating one or more voice
audio data related to the one or more speaker from the received
audio signal. In some embodiments, selectively conditioning the
audio signal may include removing audio background noise data as
described above.
[0452] At step 3766, the conditioned audio signal may be provided
to a hearing aid interface of user 100. For example, the
conditioned audio signal may be transmitted to a hearing interface
device (e.g., earpiece, headphones, speaker, etc.). Step 3766 may
be similar to step 3524 of process 3500.
[0453] FIG. 37C shows an illustrative process 3770 of transmitting
a conditioned audio signal to a device (e.g., an earpiece of the
hearing aid system). At step 3771, a processor of the hearing aid
system may be programmed to receive an audio signal from the at
least one microphone. In various embodiments, step 3771 may be the
same as step 3516? of process 3500.
[0454] At step 3772, a processor of the hearing aid system may
detect, based on analysis of the audio signals, a first audio
signal associated with a first time period, wherein the first audio
signal is representative of a voice of a single individual. In an
example embodiment, the first audio signal may correspond to a
single person communicating with user 100 during the first time
window. Alternatively, during a first time window, multiple
individuals may be communicated with user 100, having distinct
voices, that the processor of the hearing aid system is capable of
separating using any of the suitable approaches discussed above. In
some embodiments, the audio signal associated with the first time
window may be transmitted to server 250, and a processor of server
250 may separate audio signal to extract voices of individuals
communicating with user 100.
[0455] At step 3773, the processor of the hearing aid system may
detect, based on analysis of the audio signals, a second audio
signal associated with a second time period, wherein the second
time period is different from the first time period, and wherein
the second audio signal is representative of overlapping voices of
two or more individuals. For example, the second time window may
correspond to an instance when multiple speakers are talking at the
same time. In some embodiments, the first or the second time window
may not have to correspond to a continuous time interval. For
example, during a conversation, a speech of a single individual may
be overlapped at various times by the voices of other individuals.
In such cases, the time when a single individual is speaking
corresponds to the first time window and the time when multiple
individuals are speaking correspond to the second time window
[0456] At step 3774, the first detected audio signal may be
selectively conditioned using any of the suitable approaches
discussed above. In various embodiments, step 3774 may be similar
to step 3520 of process 3500. At step 3775, the second detected
audio signal may be selectively conditioned using any of the
suitable approaches discussed above. In various embodiments, the
selective conditioning of the first audio signal may be different
in at least one respect relative to the selective conditioning of
the second audio signal. For example, the first audio signal may be
amplified while the second audio signal may be suppressed. In
various embodiments, some of the voices presented in the second
audio signal may be separated and suppressed, and other voices
presented in the second audio signal may be amplified, or modified
in any suitable way as discussed above.
[0457] At step 3776, the conditioned audio signal may be provided
to the hearing aid system of user 100. For example, the conditioned
audio signal may be transmitted to a hearing interface device
(e.g., earpiece, headphones, speaker, etc.). Step 3776 may be
similar to step 3524 of process 3500.
[0458] In various embodiments, the processor of the hearing aid
system may be programmed to analyze one or more images captured by
a wearable camera of the hearing aid system and identify two or
more individuals, wherein the selective conditioning of the first
audio signal and the second audio signal is based on information
associated with an identity of at least one of the two or more
individuals. For example, if it is important for user 100 to
clearly hear the voice of the identified individual (e.g., when one
of the individuals is the user's boss) the voice of the identified
individual may be amplified. In various embodiments, one of the two
individuals may be identified using any of the suitable approaches
discussed above. For example, the individual may be identified
using a computer-based model trained to recognize people within
images. In some cases, the individual may also be identified based
on an audio signal detected within the first or the second audio
signal. For example, the hearing aid system may retrieve from the
database of server 250 various voiceprints of known individuals and
use one or more of the retrieved voiceprints to identify a voice of
a known individual within the first or the second audio signal.
[0459] In some embodiments, the hearing aid system may be
configured to condition the first and the second audio signal and
modify voices associated with identified individuals in any
suitable way as discussed above. For example, the hearing aid
system may suppress one or more voices associated with the one or
more identified individual, amplify the one or more voices, change
the pitch or the rate of the one or more voices, and the like. As
another example, when two individuals are present, and one
individual is identified, the voice of the identified individual
may be amplified, and the voice of the second individual may be
suppressed. As yet another example, the voice of the second
individual may be transcribed and displayed on a device associated
with the hearing aid system of user 100.
[0460] In various embodiments, when the audio signal contains
overlapping voices of various individuals, the hearing aid system,
may identify audio signals related to the voices, and selectively
condition the audio signal (e.g., by amplifying and suppressing
voices) using any suitable logic. For example, the hearing aid
system may amplify voices related to a particular topic, voices, of
individuals engaging in conversation with user 100, voices of
individuals engaging in conversation with a particular individual,
voices of individuals selected by user 100, and the like. In some
cases, the hearing aid system may be configured to suppress the
overlapping voices of a background conversation (e.g., a
conversation between various speakers that are not directly
conversing with user 100). In some cases, the hearing aid system
may suppress the voices that cannot be transcribed by the hearing
aid system (e.g., the voices that are cannot be clearly heard, or
voices that present no discernable useful information, such as
voices that produce sounds that can be interpreted to correspond to
words).
[0461] Identifying Information and Associated Individuals
[0462] According to embodiments of this disclosure, a hearing aid
system may recognize speakers in a surrounding environment of a
user (e.g., user 100 in FIGS. 1A-1B and 2). In some embodiments,
the hearing aid system may further recognize that one speaker is
talking to another speaker or user 100. Such recognition may be
implemented through image analysis, audio analysis, or both. The
hearing aid system may transcribe recognized conversations between
the speakers. In some embodiments, the conversations may be
associated with respective identifiers (e.g., names) of the
speakers, if the speakers are recognized individuals. In some
embodiments, the hearing aid system may capture instructions or
action items directed to user 100 from a speaker (e.g., when user
100 is in a meeting).
[0463] FIG. 38A is a block diagram illustrating a hearing aid
system 3800 according to an example embodiment. As shown in FIG.
38A, the hearing aid system 3800 includes at least one wearable
camera 3801, at least one microphone 3802, at least one processor
3803, and a memory 3804. In some embodiments, system 3800 may
further include other components, such as components as shown in
FIGS. 5A-5C.
[0464] In some embodiments, wearable camera 3801 may capture images
from an environment of user 100. In some embodiments, wearable
camera 3801 may include image sensor 220 in FIG. 5A or 5C. In some
embodiments, wearable camera 3801 may include at least one of image
sensors 220a or 220b in FIG. 5B.
[0465] In some embodiments, microphone 3802 may capture sounds from
the environment of user 100. In some embodiments, microphone 3802
may include a directional microphone. In some embodiments,
microphone 3802 may include multiple microphones (e.g., a
microphone array). In such cases, one microphone may capture only
the background noise, while another microphone may capture a
combined audio including the background noise as well as
individuals' voices. Processor 3803 may obtain the voices by
subtracting the background noise from the combined audio. In some
other embodiments, system 3800 may include at least one microphone
and a pressure sensor (not shown). The pressure sensor may encode
air pressure differences (e.g., caused by a sound wave) as a
digital signal. System 3800 may process the sounds captured by
microphone 3802 and the digital signal captured by the pressure
sensor to separate the required voice and the background noise.
[0466] In some embodiments, wearable camera 3801 and microphone
3802 may be included in a common housing (e.g., a shell). For
example, wearable camera 3801 and microphone 3802 may be included
in a common housing of apparatus 110 in FIGS. 3A-3B and 4A-4B.
[0467] In some embodiments, processor 3803 may be implemented as
processor 210 in FIG. 5A or 5C. In some embodiments, processor 3803
may be implemented as at least one of processors 210a or 210b in
FIG. 5B. In some embodiments, processor 3803 may be implemented as
processor 540 in FIG. 5C. In some embodiments, processor 3803 may
be included in the common housing that includes wearable camera
3801 and microphone 3802. For example, processor 3803 may be
included in the common housing of apparatus 110 in FIGS. 3A-3B and
4A-4B. In some embodiments, processor 3803 may be included in a
second housing separate from the common housing. In some
embodiments, the second housing may be associated with a paired
mobile device. For example, the mobile device may be computing
device 120 in FIG. 1A-1D, 2, or 5C. The mobile device may be paired
with system 300 via, for example, a wireless link (e.g.,
Bluetooth.RTM. link). In such a case, processor 3803 (e.g.,
implemented as processor 540 in FIG. 5C) may be included in a
housing of computing device 120. When processor 3803 is in the
second housing, in some embodiments, processor 3093 may receive
data (e.g., the images captured by wearable camera 3801) via a
wireless link between a transmitter (e.g., wireless transceiver
503a in FIG. 5C) in the common housing and receiver (e.g., wireless
transceiver 503b in FIG. 5C) in the second housing. For example,
the wireless link may be a Bluetooth.RTM. link, a Wi-Fi link, a
near-field communications (NFC) link, or the like.
[0468] In some embodiments, memory 3804 may be implemented as
memory 550 as shown in FIGS. 5A and 5B. In some embodiments, memory
3804 may be implemented as at least one of memories 550a and 550b
in FIG. 5C.
[0469] Apparatus 110 may be configured to deduce instructions from
an individual in the environment of user 100. FIG. 38B is a
schematic illustration showing an exemplary environment for use of
a hearing aid with instruction deduction capabilities consistent
with the present disclosure.
[0470] As shown, apparatus 110 may be configured to recognize a
face 3805 or voice 3806 associated with an individual 3807 within
the environment of user 100. For example, apparatus 110 may be
configured to capture one or more images of the surrounding
environment of user 100 using wearable camera 3801. The captured
images may include a representation of a recognized individual
3807, which may be a friend, colleague, relative, or prior
acquaintance of user 100. Processor 3803 (e.g., processors 210a
and/or 210b) may be configured to analyze the captured images and
detect the recognized user using various facial recognition
techniques. Accordingly, apparatus 110, or specifically memory 550,
may comprise one or more facial or voice recognition
components.
[0471] Processor 3803 may further be configured to determine
whether individual 3807 is recognized by user 100 based on one or
more detected audio characteristics of sounds associated with a
voice of individual 3807. Processor 3803 may determine that sound
3808 corresponds to voice 3806 of user 3807. Processor 3803 may
analyze audio signals representative of sound 3808 captured by
microphone 3802 to determine whether individual 3807 is recognized
by user 100. This may be performed using one or more voice
recognition algorithms, such as Hidden Markov Models, Dynamic Time
Warping, neural networks, or other techniques. Voice recognition
component and/or processor 3803 may access a database (not shown),
which may further include a voiceprint of one or more individuals.
Processor 3803 may perform voice recognition to analyze the audio
signal representative of sound 3808 to determine whether voice 3806
matches a voiceprint of an individual in the database. Accordingly,
the database may contain voiceprint data associated with a number
of individuals. After determining a match, individual 3807 may be
determined to be a recognized individual of user 100. This process
may be used alone, or in conjunction with the facial recognition
techniques. For example, individual 3807 may be recognized using
facial recognition and may be verified using voice recognition, or
vice versa.
[0472] After determining that individual 3807 is a recognized
individual of apparatus 110, processor 3803 may cause selective
conditioning of audio associated with the recognized individual.
The conditioned audio signal may be transmitted to a hearing
interface device (e.g., a speaker or an earphone), and thus may
provide user 100 with audio conditioned based on the recognized
individual. For example, the conditioning may include amplifying
audio signals determined to correspond to sound 3808 (which may
correspond to voice 3806 of user 3807) relative to other audio
signals. In some embodiments, amplification may be accomplished
digitally, for example by processing audio signals associated with
sound 3808 relative to other signals. Additionally, or
alternatively, amplification may be accomplished by changing one or
more parameters of microphone 3802 to focus on audio sounds
associated with individual 3807. For example, microphone 3802 may
be a directional microphone and processor 3803 may perform an
operation to focus microphone 3802 on sound 3808. Various other
techniques for amplifying sound 3808 may be used, such as using a
beamforming microphone array, acoustic telescope techniques,
etc.
[0473] In some embodiments, selective conditioning may include
attenuation or suppressing one or more audio signals received from
directions not associated with individual 3807. For example,
processor 3803 may attenuate sounds 3809 and 3810. Similar to
amplification of sound 3808, attenuation of sounds may occur
through processing audio signals, or by varying one or more
parameters associated with microphone 3802 to direct focus away
from sounds not associated with individual 3807.
[0474] Selective conditioning may further include determining
whether individual 3807 is speaking. For example, processor 3803
may be configured to analyze images or videos containing
representations of individual 3807 to determine when individual
3807 is speaking, for example, based on detected movement of the
recognized individual's lips. This may also be determined through
analysis of audio signals received by microphone 3802, for example
by detecting the voice 3806 of individual 3807. In some
embodiments, the selective conditioning may occur dynamically
(initiated and/or terminated) based on whether or not the
recognized individual is speaking.
[0475] In some embodiments, conditioning may further include
changing a tone of one or more audio signals corresponding to sound
3808 to make the sound more perceptible to user 100. For example,
user 100 may have lesser sensitivity to tones in a certain range
and conditioning of the audio signals may adjust the pitch of sound
3808. In some embodiments processor 3803 may be configured to
change a rate of speech associated with one or more audio signals.
For example, sound 3808 may be determined to correspond to voice
3806 of individual 3807. Processor 3803 may be configured to vary
the rate of speech of individual 3807 to make the detected speech
more perceptible to user 100. Various other processing may be
performed, such as modifying the tone of sound 3808 to maintain the
same pitch as the original audio signal, or to reduce noise within
the audio signal.
[0476] In some embodiments, processor 3803 may determine a region
3811 associated with individual 3807. Region 3811 may be associated
with a direction of individual 3807 relative to apparatus 110 or
user 100. The direction of individual 3807 may be determined using
wearable camera 3801 and/or microphone 3802 using the methods
described above. As shown in FIG. 38B, region 3811 may be defined
by a cone or range of directions based on a determined direction of
individual 3807. The range of angles may be defined by an angle,
.theta., as shown in FIG. 38B. The angle, .theta., may be any
suitable angle for defining a range for conditioning sounds within
the environment of user 100 (e.g., 10 degrees, 20 degrees, 45
degrees). Region 3811 may be dynamically calculated as the position
of individual 3807 changes relative to apparatus 110. For example,
as user 100 turns, or if individual 3807 moves within the
environment, processor 3803 may be configured to track individual
3807 within the environment and dynamically update region 3811.
Region 3811 may be used for selective conditioning, for example by
amplifying sounds associated with region 3811 and/or attenuating
sounds determined to be emanating from outside of region 3811.
[0477] The conditioned audio signal may then be transmitted to the
hearing interface device and produced for user 100. Thus, in the
conditioned audio signal, sound 3808 (and specifically voice 3806)
may be louder and/or more easily distinguishable than sounds 3809
and 3810, which may represent background noise within the
environment.
[0478] In some embodiments, processor 3803 may determine the
direction of a recognized individual relative to the user based on
the images. In some embodiments, processor 3803 may be configured
to determine a look direction of the individuals in the images. In
some embodiments, when the recognized individual is speaking to the
user, the selective conditioning may include amplifying an audio
signal associated with the recognized individual relative to other
audio signals received from directions outside a region associated
with the recognized individual. If the recognized individual is
speaking towards the user (e.g., individual 3807 speaking towards
user 100 in FIG. 38B), processor 3803 may transcribe text
corresponding to speech associated with the voice of the recognized
individual.
[0479] FIG. 38C illustrates a user wearing an exemplary hearing aid
system. User 100 may wear system 3800 (e.g., as a wearable device).
Wearable camera 3801 may capture images of the environment of user
100. As illustrated in FIG. 38C, a first individual 3812 may stand
in front of user 100 and look in the direction of user 100. In
addition, a second individual 3813 may also stand in front of user
100, but look in a direction away from user 100. The image sensor
of system 3800 may capture one or more images including first
individual 3812 and second individual 3813. Processor 3803 may
analyze the images captured by wearable camera 3801. Processor 3803
may also identify one or more individuals included in the images,
based on image analysis or face recognition. For example, processor
3803 may identify first individual 3812 and second individual 3813
included in the image. Based on the analysis, processor 3803 may
detect that first individual 3812 is looking in the direction of
user 100 and second individual 3813 is looking in a direction away
from user 100. Microphone 3804 may receive one or more audio
signals from the environment of user 100. For example, microphone
3804 may be configured to receive (or detect) a first audio signal
associated with the voice of the first individual 3812 and a second
audio signal associated with the voice of the second individual
3813. In the example as shown in FIG. 38C, based on the looking
directions of first individual 3812 and second individual 3813,
processor 3803 may transcribe text corresponding to speech
associated with the voice of first individual 3812, but not text
corresponding to speech associated with the voice of second
individual 3813.
[0480] In some embodiments, processor 3803 may be programmed to
perform a method for deducing instructions for user 100. FIG. 39A
is a flowchart illustrating a process 3900A for deducing
instructions for a hearing aid system according to an embodiment.
Processor 3803 may perform process 3900A to recognize an individual
in a surrounding environment of user 100 after system 300 captures
the voices or images of the individual.
[0481] At step 3902, processor 3803 may receive the images captured
by wearable camera 3801. In some embodiments, the images may
include human beings. In some embodiments, wearable camera 3801 may
capture the images substantially in line with a line of sight of
user 100, such that an individual user 100 is speaking with may be
likely to be at or near the center of the images.
[0482] At step 3904, processor 3803 receives audio signals
representative of sounds captured by microphone 3802. In some
embodiments, the audio signals may include speech or non-speech
sounds by one or more persons in the vicinity of user 100,
environmental sound (e.g., music, tones, or environmental noise),
or the like. In some embodiments, the sounds may be an audio
stream. The audio stream may be made up of a combination of audio
signal components. Each of the audio signal components may be
separated to provide a unique audio signal. Processor 3803 may then
receive a plurality of such unique audio signals.
[0483] At step 3906, processor 3803 may identify a first individual
represented in at least one of the images. In some embodiments,
processor 3803 may receive a plurality of images. In some
embodiments, step 3906 may be optional. The first individual may
appear in some or all of the plurality of images. In some
embodiments, processor 3803 may implement an image processing
technique (e.g., an algorithm or a software module) to recognize
individuals in the images Such an image processing technique may be
based on geometry. For example, processor 3803 may identify an
individual at the center of the image as the first individual. For
example, processor 3803 may identify a chin of the user in the
images and then identify another individual opposite the user.
[0484] In some embodiments, processor 3803 may amplify the first
audio signal. For example, processor 3803 may amplify the first
audio signal by changing tones or applying a noise cancellation
technique (e.g., an algorithm or a software module). In some
embodiments, processor 3803 may cause transmission (e.g., using
wireless transceiver 530 or 530a in FIGS. 5A-5C) of the amplified
first audio signal to a hearing interface device that is configured
to provide sound to an ear of user 100. By providing the sound of
the amplified first audio signal, user 100 may be able to
concentrate on the first individual with fewer disturbances of
other voices or sounds. For example, the hearing interface device
may include a speaker associated with an earpiece. For another
example, the hearing interface device may include a bone conduction
microphone.
[0485] In some embodiments, processor 3803 may transmit the
amplified first audio signal as long as the first individual keeps
speaking. Processor 3803 may transmit the amplified first audio
even if other voices or sounds are captured by microphone 3802,
whether recognized or not, in order to let user 100 continuously
listen to the first individual. In some embodiments, when the first
individual pauses for up to a predetermined length, processor 3803
may determine it as an end of a speech by the first individual and
attempt to detect speech of other individuals. In some embodiments,
processor 3803 may amplify audio signals of other individuals to a
different degree from the first audio signal.
[0486] Referring back to FIG. 39A, at step 3908, processor 3803 may
identify a first audio signal. The first audio signal may be
representative of a voice of the first individual from among the
received audio signals. However, the first audio signal may also be
associated with another, or unknown, speaker. In some embodiments,
the first audio signal may be preprocessed to be separated from
background noise captured by microphone 3802.
[0487] At step 3910, processor 3803 may transcribe and store, in
memory 3804, text corresponding to the speech, which may be
associated with the voice of the first individual, if the
individual has been associated with the speech. In some
embodiments, the voice may include speech (e.g., a conversation or
a verbal instruction). The voice may further include non-speech
sounds (e.g., laughter, crying, or noise). Processor 3803 may
implement a text-to-speech technique (e.g., a text-to-speech
algorithm or software module) to recognize the speech from the
voice and transcribe the speech associated with the voice into the
text.
[0488] At step 3912, processor 3803 may determine whether the
speaker is the first individual, and whether the first individual
is a recognized individual. In some embodiment, processor 3803 may
recognize the first individual by analyzing the first audio signal
associated. For example, the voice of the first individual may have
been previously recognized (e.g., in a different conversation or in
an earlier part of the same conversation), and features (e.g.,
vocal prints) of the recognized voice of the first individual may
be stored in memory 3804 (e.g., in a database). When processor 3804
analyzes the first audio signal, it may determine the features of
the first audio signal (e.g., by extracting vocal prints) and
search memory 3804 (e.g., in the database) to seek a match. If such
a match is found, processor 3803 may determine the matching between
the speaker and the first individual, and that the first individual
is a recognized individual.
[0489] In some embodiments, processor 3803 may recognize the first
individual based on imaged facial features extracted from the at
least one image identified at step 3803. For example, the images of
the first individual may have been previously recognized (e.g.,
using a facial recognition algorithm), and the first individual's
facial features may be stored in memory 3804 (e.g., in a database).
When processor 3804 analyzes the identified images, it may
determine the facial features of the first individual and search
memory 3804 (e.g., in the database) to seek a match. If such a
match is found, processor 3803 may determine that the first
individual is a recognized individual.
[0490] In some embodiments, processor 3803 may recognize the first
individual based on both the first audio signal and the identified
images. It should be noted that processor 3803 may also use other
methods, processes, algorithms, or means to recognize the first
individual, not limited to the examples as described herein.
[0491] Referring back to FIG. 39A, at step 3914, if the first
individual is a recognized individual, processor 3803 may associate
an identifier of the first recognized individual with the stored
text corresponding to the speech associated with the voice of the
first individual. In some embodiments, for example, if the text is
stored in a database (e.g., a relational database) in memory 3804,
processor 3803 may add or change a record in the relational
database to store the identifier as a key of the text, in which the
text is stored as a value. In some embodiments, processor 3803 may
prompt user 100 for identifying information relating to the first
individual if the first individual is not a recognized individual.
For example, processor 3803 may prompt user 100 to speak an
identifier (e.g., a name, a label, or a tag) of the first
individual. For another example, processor 3803 may prompt user 100
an input field in a user interface (e.g., on display 260 in FIG.
5C) for user 100 to input the identifier of the first
individual.
[0492] FIG. 39B is a flowchart illustrating a process 3900B for
deducing instructions for a hearing aid system according to an
embodiment. Process 3900B may follow step 3910 of process 3900A.
Processor 3803 may perform process 3900B to recognize whether the
individual is speaking towards user 100.
[0493] At step 3916, processor 3803 may determine whether the
speech associated with the voice of the first individual is
directed toward user 100. In some embodiments, processor 3803 may
determine whether the speech associated with the voice of the first
individual is directed toward user 100 based on at least one of a
detected look direction of user 100 or a detected look direction of
the first individual. For example, processor 3803 may determine the
look direction of user 100 based on detection of a chin of user 100
in at least one of the images. For another example, processor 3803
may determine the look direction of the first individual based on
detection of one or more eyes of the first individual in at least
one of the images and based on at least one characteristic of the
one or more detected eyes. For another example, processor 3803 may
determine the look direction of the first individual based on
gestures, gaits, or body movement features of the first individual
detected from at least one of the images. For another example,
processor 3803 may determine the look direction of the first
individual based on the user's name included in the speech of the
first individual.
[0494] At step 3918, if the speech of the first individual is
directed toward user 100, processor 3803 may store in memory 3804
an indication that the first individual's speech is directed toward
user 100. For example, processor 3803 may store the indication in
the relational database described at step 3914.
[0495] FIG. 40A is a flowchart illustrating a process 4000A for
deducing instructions for a hearing aid system according to an
embodiment. In some embodiments, process 4000A may follow any step
of process 3900A or 3900B. Processor 3803 may implement process
4000A to recognize multiple individuals in the surrounding
environment of user 100 and transcribe their speech if the voice or
images of the individual is captured by system 300.
[0496] At step 4002, processor 3803 may identify a second
individual represented in at least one of the images Step 4002 may
be implemented in a manner similar to step 3906. For example,
processor 3803 may use the image processing algorithm to identify
individuals in images based on the individuals' traits or
characteristics of at least one of the body shapes, motions, or
facial expressions. If recognized individuals have different traits
or characteristics, processor 3803 may determine that the second
individual is identified in the images.
[0497] At step 4004, processor 3803 may identify a second audio
signal, from among the received audio signals, representative of a
voice of the second individual. Step 4004 may be implemented in a
manner similar to step 3908. For example, processor 3803 may
extract features (e.g., vocal prints) from audio signals. If the
extracted features are not the same, processor 3803 may determine
that the second signal is identified in the received audio
signals.
[0498] At step 4006, processor 3803 may transcribe and store, in
memory 3804, text corresponding to speech associated with the voice
of the second individual. Step 4006 may be implemented in a manner
similar to step 3910.
[0499] FIG. 40B is a flowchart illustrating a process 4000B for
deducing instructions for a hearing aid system according to an
embodiment. In some embodiments, process 4000B may follow step 4006
of process 4000A. Processor 3803 may implement process 4000B to
recognize that whether the second individual is a recognized
individual. Processor 3803 may implement process 4000B to further
recognize whether the second individual is speaking towards user
100, whether the second individual is speaking towards the first
individual, or whether the first individual is speaking towards the
second individual.
[0500] At step 4008, processor 3803 may determine whether the
second individual is a recognized individual. Step 4008 may be
implemented in a manner similar to step 3912.
[0501] At step 4010, if the second individual is a recognized
individual, processor 3803 may associate an identifier of the
second recognized individual with the stored text corresponding to
speech associated with the voice of the second individual. Step
4010 may be implemented in a manner similar to step 3914.
[0502] At step 4012, processor 3803 may determine whether the
speech associated with the voice of the second individual is
directed toward user 100. Step 4012 may be implemented in a manner
similar to step 3916.
[0503] At step 4014, if the speech of the second individual is
directed toward user 100, processor 3803 may store in memory 3804
an indication that the second individual's speech is directed
toward user 100. Step 4014 may be implemented in a manner similar
to step 3918.
[0504] At step 4016, processor 3803 may determine whether the
speech associated with the voice of the second individual is
directed toward the first individual. Step 4016 may be implemented
in a manner similar to step 3916 or 4012. In some embodiments,
processor 3803 may determine whether the speech associated with the
voice of the second individual is directed toward the first
individual based on a look direction of the second individual
detected based on analysis of at least one of the images. In some
embodiments, processor 3803 may determine whether the speech
associated with the voice of the second individual is directed
toward the first individual based on detection of a name associated
with the first individual in the speech of the second
individual.
[0505] At step 4018, if the speech of the second individual is
directed toward the first individual, processor 3803 may store in
memory 3804 an indication that the second individual's speech is
directed toward the first individual. Step 4018 may be implemented
in a manner similar to step 3918 or 4014.
[0506] At step 4020, processor 3803 may determine whether the
speech associated with the voice of the first individual is
directed toward the second individual. Step 4016 may be implemented
in a manner similar to step 3916, 4012, or 4016.
[0507] At step 4022, if the speech of the first individual is
directed toward the second individual, processor 3803 may store in
memory 3804 an indication that the first individual's speech is
directed toward the second individual. Step 4022 may be implemented
in a manner similar to step 3918, 4014, or 4018.
[0508] In some embodiments, processes 39A-39B or 40A-40B may
include additional steps. For example, processor 3803 may perform
those additional steps after any step of 39A-39B or 40A-40B.
[0509] In some embodiments, processor 3803 may cause the stored
text (e.g., at step 3910 or 4006) to be shown on a display. In some
embodiments, the display (not shown in FIG. 38A) may be included in
the common housing that includes wearable camera 3801 and
microphone 3802. In some embodiments, the display may be associated
with a paired mobile device paired with system 300. For example,
the mobile device may be computing device 120 in FIG. 1A-1D, 2 or
5C. The display may be, for example, display 260 in FIG. 5C.
[0510] In some embodiments, processor 3803 may generate a task item
based on analysis of the speech associated with the voice of the
first individual. In some embodiments, processor 3803 may implement
a task-context matching technique (e.g., an algorithm or a software
module) to determine whether the context (e.g., the stored text of
the recognized individual's speech) is suitable for any task (e.g.,
an undated task or a dated task). For example, processor 3803 may
implement the task-context matching technique to recognize that the
context of the speech between the recognized individuals and user
100 is a meeting. Based on that context, processor 3803 may further
determine that the context is suitable for receiving tasks. Based
on the stored text and the direction of the speech (e.g., as
recognized in process 400B), processor 3803 may determine the
content of the task. In some embodiments, processor 3803 may
implement a suggestion technique (e.g., an algorithm or a software
module) to suggest the task to user 100. In some embodiments, the
suggestion technique may include a natural language processing
technique. For example, processor 3803 may cause to suggest
performing the task or setting a date for the task. In some
embodiments, processor 3803 may identify tasks by specific words in
the speech, such as "please", prepare", "e-mail me", "send", or the
like. In some embodiments, processor 3803 may attach a due date to
a task, based on identified times or dates in the speech, such as,
for example, "by Wednesday noon", "next week", or the like.
[0511] In some embodiments, processor 3803 may deduce or identify
instructions based on at least one of the speech, images, or the
transcribed texts. For example, processor 3803 may analyze the
context of the speech of the first individual using natural
language processing technique to determine whether there is an
instruction included. For another example, processor 3803 may
analyze the images captured and analyze gestures, gaits, facial
expressions, body movements to determine whether there is an
instruction included, such as nodding a head, shaking a head,
raising a hand, or the like. For another example, processor 3803
may analyze the transcribed texts to determine whether there is an
instruction included, such as generating a task item as previously
described. In some embodiments, processor 3803 may determine
whether there is an instruction from the first individual using a
combination of any of the speech, images, or the transcribed texts.
In some embodiments, processor 3803 may determine whether the
recognized instruction is directed to the user or to a second
individual based on who the first individual is speaking to. In
some embodiments, processor 3803 may further check with contextual
information to further determine whether there is an instruction
included. For example, when processor 3803 recognized a candidate
instruction related to adding an item to a schedule or calendar of
the user, processor 3803 may checked the schedule or calendar for
conflicts to determined that whether the item is restated or newly
added.
[0512] In some embodiments, processor 3803 may update a database
associated with user 100 to include the generated task item. For
example, the database may be the database in memory 3802 as
described in step 3912, 3914, or 3918. Processor 3803 may store or
update the generated task item as a data record in the database,
for example.
[0513] In some embodiments, processor 3803 may collect the tasks
throughout the day and provide them to the user upon request.
[0514] Selectively Conditioning of Audio Signals Based on an
Audioprint of an Object
[0515] Human beings have distinct and different voices. While some
people have a good voice memory and can easily recognize their
first primary school teacher, other people may have difficulty
recognizing their closest friends only from their voice. Nowadays,
computer algorithms surpass most people in recognizing speakers
because they can identify and distinguish the human voice. The way
these machine-learning algorithms recognize speakers is based on
mathematical solutions that use audioprints. The term "audioprint,"
also known as "acoustic fingerprint" and "voice signature," refers
to a condensed digital summary of the specific acoustic features of
a sound-emanating object (e.g., individuals and also inanimate
objects) deterministically generated from a reference audio signal.
A common technique for determining an audioprint from recorded
audio signals is using a time-frequency graph called a spectrogram.
For example, the disclosed hearing aid system may identify in the
spectrogram multiple points (e.g., peak intensity points) related
to different words or vocal sounds created by an individual talking
to user 100. The disclosed hearing aid system may access multiple
reference audioprints associated with different sound-emanating
objects stored in a local or a cloud-based database. Using the
reference audioprints, the disclosed hearing aid system may
determine an audioprint from recorded audio signals and identify
the sound-emanating object responsible for generating the audio
signals. Consistent with the present disclosure, the hearing aid
system may retrieve information relating to the identified
sound-emanating object and cause selective conditioning of at least
one audio signal associated with the identified sound-emanating
object based on the retrieved information. For example, when user
100 is at the park with his child, the disclosed system may amplify
the voice of the child relative to the voices of other nearby
children.
[0516] FIG. 41A illustrates an exemplary embodiment of a memory
4100 containing software modules consistent with the present
disclosure. In particular, as shown, memory 4100 may include an
audio analysis module 4102, an audioprint determination module
4104, a database access module 4106, a selective conditioning
module 4108, a transmission module 4110, and a database 4112.
Modules 4102, 4104, 4106, 4108, and 4110 may contain software
instructions for execution by at least one processing device (e.g.,
processor 210, included with the suggested hearing aid system).
Audio analysis module 4102, audioprint determination module 4104,
database access module 4106, selective conditioning module 4108,
transmission module 4110, and database 4112 may cooperate to
perform multiple operations.
[0517] For example, the hearing aid system may be used to
selectively condition audio signals based on a determined
audioprint of a sound-emitting object. For example, audio analysis
module 4102 may receive audio signals representative of sounds
emanating from objects in an environment of user 100 and analyze
the received audio signals to obtain an isolated audio stream
associated with one sound-emanating object. Audioprint
determination module 4104 may determine an audioprint of the
sound-emanating object from the isolated audio stream. In one
implementation, audioprint determination module 4104 may use deep
learning algorithms or neural embedding models to determine the
audioprint of the sound-emanating object. Database access module
4106 may interact with database 4112, which may store information
relating to sound-emanating objects associated with user 100 and
any other information associated with the functions of modules
4102-4110. For example, database access module 4106 may use the
determined audioprint to retrieve information relating to a
detected sound-emanating object from database 4112. The retrieved
information may include relationship level indicators between user
100 and the detected sound-emanating object, or specific audio
conditioning rules associated with an identified sound-emanating
object. Selective conditioning module 4108 may cause selective
conditioning of at least one audio signal associated with the
identified sound-emanating object. For example, selective
conditioning module 4108 may amplify sounds from the user's
smartphone and avoid from amplifying sounds from other phones.
Transmission module 4110 may cause transmission of the at least one
conditioned audio signal to a hearing interface device (e.g.,
hearing interface device 1710) configured to provide sounds to an
ear of user 100.
[0518] In another example, the hearing aid system may attenuate
background noise based on determined audioprints of objects in the
environment of user 100. For example, audio analysis module 4102
may receive audio signals representative of sounds from the
environment of user 100 and analyze the received audio signals to
isolate a plurality of audio streams associated with a
corresponding plurality of sound-emanating objects in the
environment of user 100. Each sound-emanating object in the
environment of user 100 may be associated with a unique audioprint.
Audioprint determination module 4104 may determine a plurality of
audioprints associated with the plurality of isolated audio
streams. Database access module 4106 may use the determined
audioprints to obtain at least one indicator of a type associated
with each of the plurality of sound-emanating objects. The at least
one indicator may be indicative of a level of interest user 100 has
with the type associated with each of the plurality of
sound-emanating objects. Selective conditioning module 4108 may
cause selective conditioning of the plurality of isolated audio
streams based on the determined at least one indicator of a type
associated with each of the plurality of sound-emanating objects.
For example, selective conditioning module 4108 may selectively
attenuate a first audio stream determined to be associated with
background noise relative to a second audio stream determined to
not be associated with background noise. Transmission module 4110
may cause transmission of the conditioned audio signals (and the
non-conditioned audio signals) to a hearing interface device
configured to provide sounds to an ear of user 100. Additional
details on this example operation are provided below with reference
to FIGS. 44A-44C.
[0519] Consistent with embodiments of the present disclosure,
memory 4100 may also include an object identification module (not
shown). The object identification module may identify the at least
one sound-emanating object in the environment of user 100 based on
the audioprint determined by audioprint determination module 4104.
Moreover, in an embodiment, the object identification module may
identify the at least one sound-emanating object in the environment
of user 100 using image analysis. For example, the object
identification module may receive a plurality of images depicting
one or more sound-emanating objects. The plurality of images may be
captured by a wearable camera located in a same housing that
includes the wearable microphone (e.g., apparatus 110). According
to this embodiment, the object identification module may determine
visual characteristics of a sound-emanating object based on
analysis of the plurality of images. Thereafter, the object
identification module may use the determined visual characteristics
and the determined audioprint to identify the sound-emanating
object. For example, the visual characteristics of the
sound-emanating object may include facial features of an individual
speaking with user 100. Database access module 4106 may retrieve
from database 4112 predefined settings associated with an identity
of the sound-emanating object. Thereafter, selective conditioning
module 4108 may cause the conditioning of at least one audio signal
based on the identity of the sound-emanating object. For example,
sounds generated by specific sound-emitting objects may be silenced
(e.g., the intensity of the conditioned audio signals associated
with the AC may be 0%) and sounds generated by other specific
sound-emitting objects may be amplified (e.g., the intensity of the
conditioned audio signals associated with a family member may be
110%).
[0520] After the object identification module identifies the
sound-emanating object (e.g., using audio analysis, using image
analysis, or using a combination of both), database access module
4106 may retrieve predefined settings related to the identified
sound-emanating object. In one embodiment, the predefined settings
may be associated with modifications to audio signals generated by
the sound-emanating object. For example, according to one
predefined setting, audio signals from a specific sound-emanating
object may be silenced whenever encountered. In another embodiment,
the predefined settings may be defined by or be specific to user
100. For example, Alice may want to amplify sounds generated by
babies and Bob may want to silence sounds generated by babies. In
another embodiment, the predefined settings may be context related.
In other words, a single sound-emanating object may be associated
with different settings that correspond with different situations.
Thus, the hearing aid system may identify the situation user 100 is
in and apply the settings associated with sound-emanating objects
accordingly. As another example, when Alice is at home, she may
want to amplify sounds generated by babies; but when Alice is at
work, she may want to silence sounds generated by babies. The
hearing aid system may determine if Alice is at home or at work
(e.g., through analysis of images captured of Alice's surrounds, by
accessing information associated with a calendar or schedule,
and/or via accessing GPS or other location information) and modify
the audio signals associated with babies accordingly.
[0521] Consistent with additional embodiments of the present
disclosure, the software modules illustrated in FIG. 41A may be
stored in separate memory devices. In one example embodiment,
selective conditioning module 4108 may be stored in a memory device
located in a hearing interface device (e.g., hearing interface
device 1710). The hearing interface device in this disclosure may
include an electroacoustic transducer configured to provide sounds
from the at least one audio signal to an ear of user 100. The
electroacoustic transducer may include a speaker or a bone
conduction microphone. In this example embodiment, transmission
module 4110 may transmit isolated audio signals to the hearing
interface device, and the conditioning of the audio signals may be
performed by the hearing interface device. In another example
embodiment, the hearing interface device may include a receiver
configured to receive at least one audio signal, wherein the at
least one audio signal was acquired by a wearable microphone and
was selectively conditioned by at least one processor (e.g.,
processor 210 located in apparatus 110) configured to identify an
audioprint of the at least one sound-emanating object using a
plurality of reference audioprints, retrieve information from a
database about the at least one sound-emanating object, and cause
the conditioning based on retrieved information.
[0522] FIG. 41B is a schematic illustration showing an exemplary
environment 4150 for using a hearing aid system 4160 consistent
with the present disclosure. Hearing aid system 4160 may include
apparatus 110 and identify one or more individuals within
environment 4150 using wearable microphone 1720 and hearing
interface device 1710 to provide selectively conditioned audio
signals to an ear of user 100. In the illustrated scenario,
apparatus 110 may identify a first individual 4152 and a second
individual 4154 using audioprints determined from recorded audio
signals. For example, wearable microphone 1720 may record audio
signals generated by sound-emitting objects in environment 4150. In
some embodiments, the audio signals may represent voices of various
individuals. For example, as shown in FIG. 41B, first audio signals
4156 may represent a voice of first individual 4152 and second
audio signals 4158 may represent a voice of second individual 4154.
The at least one processing device of hearing aid system 4160 may
analyze first audio signals 4156 and second audio signals 4158 to
separate them and to determine audioprints associated with voices.
For example, the at least one processing device may use one or more
speech or voice activity detection (VAD) algorithms and/or the
voice separation techniques to isolate audio signals associated
with each voice. In some embodiments, the at least one processing
device may perform further analysis on the audio signal associated
the detected voice activity to determine the audioprint associated
with each voice. For example, the at least one processing device
may use one or more voice recognition algorithms (e.g., Hidden
Markov Models, Dynamic Time Warping, neural networks, or other
techniques) to determine the audioprint associated with each voice.
In some embodiments, as illustrated in FIG. 43B, the at least one
processing device may use captured images and one or more image
recognition algorithms to identify an object, and thereafter the
object's identity may be used to determine the audioprint.
[0523] FIGS. 42A-42F are schematic illustrations of audio signals
recorded during the scenario illustrated in FIG. 41B and being
processed by at least one processing device using the software
modules depicted in FIG. 41A. In accordance with the present
disclosure, audio analysis module 4102 may receive audio signals
acquired by wearable microphone 1720 that reflect sounds generated
by first individual 4152 and second individual 4154. FIG. 42A
illustrates audio stream 4200 acquired by wearable microphone 1720.
Audio analysis module 4102 may also analyze audio stream 4200 to
identify first audio signals 4156 associated with first individual
4152 and second audio signals 4158 associated with second
individual 4154. FIG. 42B depicts first audio signal 4156 in light
gray and second audio signal 4158 in dark gray. Audio analysis
module 4102 may further isolate first audio signal 4156 associated
with first individual 4152 and second audio signal 4158 associated
with second individual 4154. FIG. 42C depicts the two isolated
audio signals. After the at least one processing device determines
the audioprints for audio signals 4156 and 4158, identifies
individuals 4152 and 4154, and retrieves from database 4112
information relating to individuals 4152 and 4154, selective
conditioning module 4108 may cause selective conditioning of first
audio signal 4156 and second audio signal 4158. In the example
illustrated in FIG. 42D, the retrieved information may indicate
that second individual 4154 is more important to user 100 than
first individual 4152. Accordingly, selective conditioning module
4108 may attenuate first audio signal 4156 and amplify second audio
signal 4158. First conditioned audio signal 4202 was generated from
first audio signal 4156 and second conditioned audio signal 4204
was generated from second audio signal 4158. Transmission module
4110 may receive first conditioned audio signal 4202 and second
conditioned audio signal 4204 from selective conditioning module
4108 and may combine them together to a conditioned audio stream
4206, as illustrated in FIG. 42E. Thereafter, transmission module
4110 may cause transmission of conditioned audio stream 4206 to
hearing interface device 1710 configured to provide sounds to an
ear of user 100. FIG. 42F depicts conditioned audio stream 4206 as
received by hearing interface device 1710. Consistent with the
present disclosure, the at least one processing device may cause
transmission of conditioned audio stream 4206 to hearing interface
device 1710 in less than 100 mSec after audio stream 4200 was
acquired by the wearable microphone. For example, conditioned audio
stream 4206 may be transmitted to the hearing interface device in
less than 50 mSec, less than 30 mSec, less than 20 mSec, or less
than 10 mSec after audio stream 4200 was acquired by the wearable
microphone.
[0524] FIG. 43A is a flowchart showing an exemplary process 4300
for selectively conditioning audio signals associated with a
recognized object consistent with the disclosed embodiments.
Process 4300 may be performed by one or more processors associated
with apparatus 110, such as processor 210. In some embodiments,
some or all of process 4300 may be performed by devices external to
apparatus 110, such as hearing interface device 1710 or computing
device 120. In other words, the processing device performing
process 4300 may include at least one processor located in a single
housing including the wearable camera and the wearable microphone,
or a plurality of processors located in separate housings.
[0525] In step 4302, the processing device may receive audio
signals acquired by a wearable microphone. The audio signals may be
representative of sounds emanating from objects in an environment
of user 100. Consistent with the present disclosure, the received
audio signals may include any form of data generated in response to
sounds within a range of between 10 to 30,000 hertz (e.g., between
20 to 20,000 hertz) in the environment of user 100. For example,
the audio signals may represent sounds generated by multiple
sound-emanating objects. Consistent with the present disclosure,
the wearable microphone may include one or more directional
microphones, a microphone array, a multi-port microphone, or
various other types of microphones. The processing device may be
configured to determine a directionality of sounds in the
environment of user 100. Accordingly, the audio signals may be
indicative of a region of the environment of user 100 associated
with a sound-emanating object that generated the sounds represented
by the audio signals.
[0526] In step 4304, the processing device may analyze the received
audio signals to obtain an isolated audio stream associated with a
sound-emanating object in the environment of user 100. In one
embodiment, the processing device may analyze the received audio
signals by using audio sample convolution. Specifically, speaker
separation and other audio analysis algorithms described in this
disclosure may use audio sample convolution. For example, by
convoluting past samples when calculating a value for a present
sample, and avoiding waiting for future samples, the delay
providing the analysis results may be significantly reduced. For
example, the delay in generating an isolated audio stream (or any
other processed audio stream) for each sound-emitting object may be
less than 50 mSec (e.g., less than 10 mSec, less than 5 mSec, or
less than 1 mSec). In the scenario illustrated in FIG. 41B, the
processing device may generate an isolated audio stream for each of
the two speakers in front of user 100. Each isolated audio stream
may include of the voice of a speaker isolated from any other
sounds such as background noises or other voices.
[0527] In step 4306, the processing device may determine an
audioprint from the isolated audio stream. In one embodiment, the
determined audioprint may be a voiceprint associated with an
individual. In another embodiment, the determined audioprint may be
associated with a non-human sound-emitting object, such as, AC, a
car, an animal, etc. The determination of the audioprint may be
performed by extracting spectral features, also referred to as
spectral attributes, spectral envelope, or spectrogram from the
isolated audio stream. In one embodiment, the isolated audio stream
may be inputted into a computer-based model such as a pre-trained
neural network, which outputs audioprint based on the extracted
features. The determined audioprint may be used to identify the
sound-emitting object to cause selective conditioning of its
associated audio signals. Consistent with the present disclosure,
the processing device may access at least one identification
database storing a set of reference audioprints for different
sound-emanating objects. The set of reference audioprints may be
previously determined by the processing device, or determined by a
different processing device. The set of reference audioprints may
be used in determining the audioprint of the sound-emitting object.
For example, the processing device may select the most similar
audioprint from the set of reference audioprints as the determined
audioprint of the sound-emitting object. In another embodiment, the
set of reference audioprints may be used in identifying the
sound-emanating object. For example, the processing device may
trigger a comparison between the determined audioprint and the set
of reference audioprints to determine an identity of the
sound-emanating object.
[0528] In some cases, when the determined audioprint matches one of
the sets of reference audioprints, the processing device may cause
the conditioning of at least one audio signal based on predefined
settings associated with the identity of the sound-emanating
object. In other cases, when the determined audioprint fails to
match any of the sets of reference audioprints, the processing
device may determine at least one indicator of a level of
similarity between a specific reference audioprint and the
determined audioprint. Based on a comparison of the at least one
indicator of a level of similarity with a predetermined threshold,
the processing device may cause the conditioning of at least one
audio signal based on predefined settings associated with the
specific reference audioprint. In addition, when the determined
audioprint fails to match any of the sets of reference audioprints,
the processing device may determine at least one indicator of a
level of similarity between a specific reference audioprint and the
determined audioprint. Based on a comparison of the at least one
indicator of a level of similarity with a predetermined threshold,
the processing device may update the set of reference audioprints
based on the determined audioprint. In some embodiments, the set of
reference audioprints may include a plurality of reference
audioprints associated with a single sound-emitting object. For
example, the set of reference audioprints may include a first
reference audioprint for a specific individual determined based on
an instance where the specific individual was standing next to user
100, and a second reference audioprint for the specific individual
determined based on an instance where a voice of the specific
individual was projected from a communication device.
[0529] In step 4308, the processing device may use the audioprint
to retrieve from a database information relating to the
sound-emanating object. In one embodiment, the retrieved
information may be indicative of a pre-existing relationship
between user 100 and the sound-emanating object. Accordingly, the
processing device cause selective conditioning of at least one
audio signal based on the pre-existing relationship. For example,
the processing device may apply a hierarchy of amplifications to
audio signals associated with a plurality of sound-emanating
objects having various levels of pre-existing relationships.
Consistent with the present disclosure, the retrieved information
may include at least one predefined audio conditioning parameter
value to apply to the audio stream associated with the
sound-emanating object. The at least one predefined audio
conditioning parameter may include pitch, loudness, cadence,
smoothness, intonation, and more. In one embodiment, the at least
one predefined audio conditioning parameter value included in the
retrieved information may be dependent on an audio hierarchy and
the sound-emanating object's position in the audio hierarchy (e.g.,
a fire alarm may be ranked higher than office chatter). In a first
example, the value of the retrieved at least one predefined audio
conditioning parameter may cause amplification of audio signals
associated with a particular sound-emanating object at a level
higher than for another sound-emanating object lower on the audio
hierarchy than the particular sound-emanating object. In a second
example, the value of the retrieved at least one predefined audio
conditioning parameter may cause attenuation of the audio signals
associated with a particular sound-emanating object at a level
lower than for another sound-emanating object higher on the audio
hierarchy than the particular sound-emanating object. In a third
example, the value of the retrieved at least one predefined audio
conditioning parameter may cause a change in tone associated with
the audio signals associated with a particular sound-emanating
object. In this example, objects higher in the hierarchy may
receive tone modification while lower ranked objects may receive no
tone change.
[0530] In step 4310, the processing device may cause selective
conditioning of at least one audio signal received by the wearable
microphone from a region associated with the at least one
sound-emanating object. Consistent with the present disclosure, the
processing device may determine a type associated with the
sound-emanating object based on a comparison of the determined
audioprint with a set of reference audioprints. The type associated
with the sound-emanating object may include mechanical machines,
speakers, humans, animals, inanimate objects, weather-related
objects, and more. After determining the type associated with the
sound-emanating object, the processing device may cause the
selective conditioning of the at least one audio signal based on
the determined type. In addition, the processing device may analyze
the received audio signals to isolate audio packets determined to
be associated with multiple sound-emanating objects in the
environment of user 100. For example, the multiple sound-emanating
objects may include a first individual and a second individual.
Accordingly, the processing device may cause a first selective
conditioning of audio signals associated with the first individual
based on retrieved information associated with the first
individual, and cause a second selective conditioning, different
from the first selective conditioning of audio signals, associated
with the second individual based on retrieved information
associated with the second individual. For example, amplifying
audio signals associated with the first individual and pitch
enhancement to audio signals associated with the second individual.
FIG. 42D shows how the processing device may cause a first
selective conditioning to audio signals associated with the first
individual and a second selective conditioning to audio signals
associated with the second individual.
[0531] In step 4312, the processing device may cause transmission
of the at least one conditioned audio signal to a hearing interface
device configured to provide sounds to an ear of user 100.
Consistent with the present disclosure, the processing device may
cause a transmitter (e.g., wireless transceiver 530a) to transmit
the conditioned audio signals to the hearing interface device via a
wireless network (e.g., cellular, Wi-Fi, Bluetooth.RTM., etc.), or
via near-field capacitive coupling, other short-range wireless
techniques, or via a wired connection. In addition, the processing
device may cause transmission of unprocessed audio signals together
with the conditioned audio signals to the hearing interface
device.
[0532] FIG. 43B is a flowchart showing another exemplary process
4350 for selectively conditioning audio signals associated with a
recognized object consistent with the disclosed embodiments.
Similar to process 4300, process 4350 may be performed by one or
more processors associated with apparatus 110 or by devices
external to apparatus 110. In other words, the processing device
performing process 4350 may include at least one processor located
in a single housing including the wearable camera and the wearable
microphone, or a plurality of processors located in separate
housings.
[0533] In step 4352, the processing device may receive a plurality
of images from an environment of user 100 captured by a wearable
camera. For example, the suggested system may include a processor
(e.g., processor 210) configured to receive a plurality of images
of the environment of user 100 captured by an image sensor (e.g.,
image sensor 220). Consistent with the present disclosure, the
plurality of images may include frames of a video stream captured
by the wearable camera.
[0534] In step 4354, the processing device may process the
plurality of images to detect a sound-emanating object in at least
one of the plurality of images, and in step 4356, the processing
device may identify the sound-emanating object using the at least
one of the plurality of images. As used herein, the term "detecting
a sound-emanating object" may broadly refer to determining an
existence of the sound-emanating object. For example, the system
may determine the existence of a plurality of distinct
sound-emanating objects. By detecting the plurality of
sound-emanating objects, the system may acquire different details
relative to the plurality of sound-emanating objects (e.g., how
many sound-emanating objects are present in the environment of user
100), but it does not necessarily gain knowledge of the type of
object. In contrast, the term "identifying a sound-emanating
object" may refer to determining a unique identifier associated
with a specific sound-emanating object that allows the system to
uniquely access records associated with the sound-emanating object
in a database (e.g., database 4112). In some embodiments, the
identification may at least in part be made based on visual
characteristics of the sound-emanating object derived from images
captured by the wearable camera. For example, the sound-emanating
object may be an individual speaking with user 100 and the visual
characteristics of the sound-emanating object may include facial
features of the individual. The unique identifier may include any
combinations of numbers, letters, and symbols. Consistent with the
present disclosure, the terms "determining a type of a
sound-emanating object" may also be used interchangeably in this
disclosure with reference to the term "identifying a
sound-emanating object."
[0535] In step 4358, the processing device may use the determined
identity of the sound-emanating object to retrieve from a database
information relating to the sound-emanating object. In one
embodiment, the retrieved information may include a reference
audioprint or voice print associated with the recognized
sound-emanating object. In another embodiment, the retrieved
information may be indicative of a pre-existing relationship
between user 100 and the recognized sound-emanating object.
Accordingly, the processing device may cause selective conditioning
of at least one audio signal based on the pre-existing
relationship. Additional details on how the retrieved information
may be used to cause selective conditioning of at least one audio
signal are described above with reference to step 4308.
[0536] In step 4360, the processing device may receive at least one
audio signal acquired by a wearable microphone, wherein the at
least one audio signal is representative of sounds emanating from
the sound-emanating objects. Consistent with some embodiments of
the present disclosure, the identities of one or more of the
sound-emanating objects may be determined based on the received
images from the wearable camera and the at least one audio signal
acquired by the wearable microphone. For example, the at least one
audio signal may be used together with the received imaged to
identify one or more sound-emanating objects when a confidence
score corresponding to a degree of certainty that a sound emanating
object represented in the captured images corresponds to one or
more objects in database is below a certain threshold.
[0537] In step 4361, the processing device may use retrieved
information (e.g., the information retrieved in step 4358) to
process at least one audio signal (e.g., the at least one audio
signal received in step 4360). In one embodiment, when the
sound-emanating object is an individual, the retrieved information
may include at least one detail about the individual (e.g., gender,
age, ethnicity, etc.). The processing device may use the at least
one detail about the individual to separate sounds associated with
the individual from sounds emanating from other sound emanating
objects. In another embodiment, the retrieved information may
include a reference audioprint associated with the recognized
sound-emanating object and the processing device may use the
reference audioprint to identify and separate sounds associated
with the recognized sound emanating object from sounds emanating
from other sound emanating objects. Consistent with the present
disclosure, audio separation may be more efficient when the
retrieved information includes a reference audioprint of a
recognized individual, but for some implementations of the system
at least one detail about the individual may be sufficient.
[0538] In step 4362, the processing device may cause selective
conditioning of the audio signal received by the wearable
microphone from a region associated with the at least one
sound-emanating object as separated in step 4361. Thus, in this
example, only the audio emanating from a particular object, for
example a person the user is speaking with, may be conditioned. For
example, the audio may be amplified.
[0539] In step 4364 the processing device may cause transmission of
the at least one conditioned audio signal to a hearing interface
device configured to provide sounds to an ear of user 100. The
details described above with reference to steps 4310 and 4312 are
relevant also for steps 4362 and 4364.
[0540] Selective Modification of Background Noises
[0541] Users of hearing aids systems typically find it intrusive
when irrelevant background noises are amplified. Some existing
hearing aids systems filter out low-frequency sounds to reduce
background noises. This solution eliminates some of the background
noises, but it provides a partial solution as it may eliminate
important parts of speech sounds or other sounds in the environment
of user 100. Other existing hearing aids systems use directional
microphones to reduce the sounds from beside and behind the user.
This solution provides a better signal-to-noise ratio in certain
specific scenarios, but it also provides a partial solution, as
some background noises are important and should not be eliminated.
The disclosed hearing aid system may include a wearable device
(e.g., apparatus 110) that causes selective conditioning of audio
signals generated by a sound-emanating object in the environment of
the user, and a hearing interface device (e.g., hearing interface
device 1710) to provide selectively modified sounds to an ear of
user 100. The disclosed hearing aid system may use image data to
determine if the background noises are important and cause
selective conditioning accordingly. For example, the hearing aid
system may amplify background noises determined to be important and
attenuate background noises determined not to be important.
[0542] FIG. 44A illustrates a scenario where user 100 is working at
his desk. User 100 wears a hearing aid system 4400 that may include
wearable camera 4402, wearable microphone 4404, and hearing
interface device 4406. In the illustrated scenario, a first part of
the environment of user 100 is associated with field of view 4408
of wearable camera 4402 and may include at least one
sound-emanating object, and a second part of the environment of
user 100 may also include at least one sound-emanating object
outside field of view 4408 of wearable camera 4402. For example,
the first part of the environment of user 100 may include a first
sound-emanating object 4410A (e.g., a computer with speakers), and
the second part of the environment of user 100 may include a second
sound-emanating object 4412A (e.g., a woman) and a third
sound-emanating object 4414A (e.g., a television).
[0543] FIG. 44B illustrates audio signals 4416 acquired by wearable
microphone 4404 during a time period T. As shown in the figure,
acquired audio signals 4416 include first audio signals 4410B from
first sound-emanating object 4410A, second audio signals 4412B from
second sound-emanating object 4412A, and third audio signals 4414B
from third sound-emanating object 4414A. In the scenario described
above, hearing aid system 4400 may determine that the sounds from
second sound-emanating object 4412A are more important that the
sounds from third sound-emanating object 4414A, and attenuate third
audio signals 4414B generated by third sound-emanating object
4414A. FIG. 44C illustrates conditioned audio signals 4418
transmitted to hearing interface device 4406. Conditioned audio
signals 4418 includes first audio signals 4410C, second audio
signals 4412C, and third audio signals 4414C. In the illustrated
example, only the third audio signals 4414C are conditioned;
specifically, third audio signals 4414C were attenuated because
hearing aid system 4400 determined that the sounds from second
sound-emanating object 4412A are more important that the sounds
from third sound-emanating object 4414A.
[0544] In one embodiment, hearing aid system 4400 may use image
data captured by wearable camera 4402 during time period T to
determine the importance of the audio signals. For example, hearing
aid system 4400 may determine from the image data that user 100 is
sitting in his office and use this information to identify the
woman based on her voice as his supervisor. Hearing aid system 4400
may determine the importance of the sounds from the woman based on
her identity. In another embodiment, the image data may be captured
by wearable camera 4402 before time period T. For example, while
user 100 walked to his desk, or sat at his desk and turned around,
wearable camera 4402 captured at least one image of the woman
participating in an activity. Hearing aid system 4400 may determine
the importance of the sounds from the woman based on the activity
the woman participated in.
[0545] FIG. 45 is a block diagram illustrating the components of
hearing interface device 4406 configured to communicate with
apparatus 110 and computing device 120, according to example
embodiments. As shown in FIG. 45, hearing interface device 4406 may
include a receiver 4500, an electroacoustic transducer 4502, a
processor 4504, a memory 4506, and a mobile power source 4508.
Receiver 4500 may be used to receive data (e.g., audio signals,
data about sound-emitting objects, and more) from apparatus 110
and/or from computing device 120. Electroacoustic transducer 4502
may be used to generate sounds based on the received data. The
generated sounds may be provided to an ear of user 100. In one
embodiment, electroacoustic transducer 4502 may include a speaker.
In another embodiment, electroacoustic transducer 4502 may include
a bone conduction microphone. Processor 4504, memory 4506, and
mobile power source 4508 may operate in a manner similar to
processor 210, memory 550, and mobile power source 520 described
above. As will be appreciated by a person skilled in the art,
having the benefit of this disclosure, numerous variations and/or
modifications may be made to hearing interface device 4406. Not all
of the components included in the illustrated configuration of
hearing interface device 4406 are essential for the operation of
hearing aid system 4400. Any component may be located in any
appropriate apparatus and the components may be rearranged into a
variety of configurations while providing the functionality of the
disclosed embodiments. For example, in one configuration, hearing
interface device 4406 may include a processor for selective
conditioning of received audio signals. In another configuration,
hearing interface device 4406 may receive audio signals selectively
conditioned by a processor located in a separate device (e.g.,
apparatus 110 or computing device 120).
[0546] In one embodiment, receiver 4500 may receive at least one
audio signal. The at least one audio signal may have been acquired
by a wearable microphone (e.g., wearable microphone 4404). The at
least one audio signal may have been selectively conditioned by at
least one processor (e.g., processor 210 or processor 540). The at
least one processor may receive a plurality of images captured by a
wearable camera (e.g., wearable camera 4402) and determine, based
on an analysis of the plurality of images, that the at least one
sound was generated by a remote sound-emanating object outside of a
field of view of the wearable camera. Thereafter, the at least one
processor may cause the conditioning of the audio signal based on
information about the remote sound-emanating object retrieved from
at least one memory (e.g., memory 550a, memory 550b, or memory
4506). Consistent with embodiments of the present disclosure,
processor 4504 of hearing interface device 4406 may have at least
some of the capabilities of: selective conditioning audio signals,
processing image data to identify objects, and processing audio
data to recognize sounds. Accordingly, the functionality described
in this disclosure with reference to a processing device located in
apparatus 110 may be also executed by processor 4504. For example,
receiver 4500 may receive nonconditioned audio signals acquired by
wearable microphone 4404 and, thereafter, processor 4504 may
determine the importance of at least one audio signal and cause
selective conditioning of the at least one audio signal based on
information retrieved from at least one memory.
[0547] FIG. 46A is an illustrative process 4600 for causing
selective modification of background noises based on determined
importance levels. The importance level assigned to audio signals
associated with a background noise may represent the likelihood
that user 100 may be interested in hearing said background noise.
Consistent with the present disclosure, the importance levels
assigned to audio signals associated with background noises may be
determined based on the content of the background noises, the
identity of the sound-emitting objects that generate the background
noises, the context of the background noises, and more. For
example, the importance level of a background noise may be
classified into multiple levels, such as "nuisance," "relevant,"
and "critical." Alternatively, the importance level may be
represented as a numeric value between one and ten, where one is
not important at all and ten is very important.
[0548] At step 4602, the hearing aid system (e.g., hearing aid
system 4400) may receive audio signals including background noises.
The background noises may include sounds from one or more
sound-emanating objects in the environment of user 100, but outside
of field of view 4408, for example, sounds from second
sound-emanating object 4412A and sounds from third sound-emanating
object 4414A. The audio signals may be received separately, for
example through directional microphones. Additionally, or
alternatively, the audio signals may be received together and
thereafter separated, using for example known audio prints of a
specific human, known patterns, for example the sound on an A/C, or
the like.
[0549] At step 4604, the hearing aid system may identify one or
more sound-emanating objects responsible for at least some of the
background noises. Consistent with the present disclosure, the
hearing aid system may identify the sound-emanating object using
information from one or more images captured by wearable camera
4402. For example, the hearing aid system may determine from
captured image data that user 100 is at his home and use this
information to determine the identity of the sound-emanating object
responsible for at least some of the background noises.
[0550] At step 4606, the hearing aid system may determine an
importance level associated with the sounds from the
sound-emanating object. In one embodiment, the determination of the
importance level may be based on a detected voiceprint of the
sound-emanating object. In another embodiment, the determination of
the importance level may be based on an analysis of a plurality of
images captured by wearable camera 4402. For example, when wearable
microphone 4404 detects audio signals associated with a car honk
when user 100 walks down the street, the hearing aid system may
rank the importance level to these audio signals as 7.5.
Alternatively, when wearable microphone 4404 detects audio signals
associated with a car honk when user 100 sits in a restaurant, the
hearing aid system may rank the importance level to these audio
signals as 2.8. In addition, the determination of the importance
level may be based on context derived from analysis of at least one
image captured by wearable camera 4402 before receiving the
background noises. For example, the hearing aid system may
determine that user 100 may put a baby in bed based on image
analysis and, five minutes later, wearable microphone 4404 may
detect a baby crying. In this case, the hearing aid system may
categorize the audio signals associated with the baby crying as
"critical." In another embodiment, the determination of the
importance level may be based on context derived from audio signals
representative of sounds acquired before receiving the sounds from
the sound-emanating object. For example, a person related to user
100 may have asked user 100 to take care of a baby and, five
minutes later, wearable microphone 4404 may detect a baby crying.
In this case, the hearing aid system may categorize the audio
signals associated with the baby crying as "critical." In another
case, the hearing aid system may determine user 100 is on a plane
and that the baby crying in the background is not related to user
100. In this case, the hearing aid system may categorize the audio
signals associated with the baby crying as "nuisance."
[0551] At step 4608, the hearing aid system may determine if the
importance level is greater than a threshold based on retrieved
information. The term "threshold" is used herewith to denote a
reference value, a level, a point, or a range of values such that
when the importance level is above it the hearing aid system may
follow a first course of action and when the importance level is
under it the hearing aid system follows a second course of action.
The value of the threshold may be predetermined for each
sound-emitting object or dynamically selected based on the context
determined based on image data.
[0552] If the importance level is determined to be less than the
threshold, the hearing aid system may cause a first selective
conditioning to weaken audio signals associated with the
sound-emanating object (step 4610). In one embodiment, when the
hearing aid system determines that an importance level of the
sound-emanating object is lower than the threshold, the first
selective conditioning may include attenuating audio signals
associated with the sound-emanating object. For example, background
noises from the AC may be considered to be unimportant, so they can
be silenced relative to sounds from other sound-emanating objects.
Alternatively, the first selective conditioning may include
amplifying audio signals associated with other sound-emanating
objects. Consistent with the present disclosure, the hearing aid
system may determine that the importance level of the at least one
sound is lower than the threshold based on analysis of a plurality
of images from wearable camera 4402.
[0553] If the importance level is determined to be greater than the
threshold, the hearing aid system may cause a second selective
conditioning to intensify audio signals associated with the
sound-emanating object (step 4612). In one embodiment, when the
hearing aid system determines that an importance level of the
sound-emanating object is greater than the threshold, the second
selective conditioning may include amplifying audio signals
associated with the sound-emanating object. For example, background
noises from certain colleagues may be considered to be important,
so they can be amplified relative to sounds from other
sound-emanating objects. Alternatively, the second selective
conditioning may include attenuating audio signals associated with
other sound-emanating objects.
[0554] In one embodiment, the hearing aid system may determine that
the background noises were generated by a plurality of
sound-emanating objects outside the field of view of the wearable
camera. Consistent with this embodiment, the hearing aid system may
identify the plurality of sound-emanating objects and rank the
plurality of remote sound-emanating objects based on their
corresponding importance levels. Thereafter, the hearing aid system
may cause selective conditioning of a plurality of sounds
associated with the plurality of remote sound-emanating objects
based on their corresponding importance levels. For example, with
reference to FIG. 44A, the hearing aid system may rank the sounds
from third sound-emanating object 4414A with a lower importance
level than the sounds from third sound-emanating object 4414.
Accordingly, the audio signals from second sound-emanating object
4412A may be amplified and the audio signals from third
sound-emanating object 4414A may be attenuated.
[0555] After selective conditioning of audio signals associated
with the sound-emanating object, the hearing aid system may provide
the conditioned audio signals to user 100 (step 4614). The
conditioned audio signals may be provided to user 100 using
electroacoustic transducer 4502 of hearing interface device 4406.
In one embodiment, the hearing aid system may notify user 100 about
background noises that were substantially removed in the
conditioned audio signals. For example, the hearing aid system may
send to computing device 120 an indication about at least one
sound-emitting object that its audio signals were attenuated. After
receiving a feedback from user 100 regarding the at least one
sound-emitting object, hearing aid system may avoid attenuating
audio signals of the at least one sound-emitting object in the
future.
[0556] FIG. 46B is a flowchart showing an exemplary process 4650
for selective modification of different types of background noises
consistent with disclosed embodiments. Process 4650 may be
performed by one or more processors associated with apparatus 110,
such as processor 210. In some embodiments, some or all of process
4650 may be performed by processors external to apparatus 110, such
as processor 4504 in hearing interface device 4406 or processor 540
in computing device 120. In other words, the at least one processor
performing process 4650 may be included in the same common housing
as wearable camera 4402 and wearable microphone 4404 or may be
included in a separate housing.
[0557] In step 4652, a processing device (e.g., processor 210) may
receive image data from an environment of user 100 captured by
wearable camera 4402 during a time period. Consistent with the
present disclosure, the received image data may include any form of
data retrieved from optical signals in the near-infrared, infrared,
visible, ultraviolet spectrums or multi-spectral. The image data
may include video clips, one or more images, or information derived
from processing one or more images. For example, the image data may
include details about objects (e.g., sound-emanating objects and
non-sound-emanating objects) identified in images captured by
wearable camera 4402.
[0558] In step 4654, the processing device may receive at least one
audio signal representative of sounds acquired by wearable
microphone 4404 during the time period. Consistent with the present
disclosure, wearable microphone 4404 may include microphone array
and/or at least one directional microphone for capturing sounds
from at least one sound-emanating object in the environment of user
100. As used herein, the term "sound-emanating object" may refer to
any object capable of generating sounds within a range of between
10 to 30,000 hertz (e.g., between 20 to 20,000 hertz). Examples of
sound-emanating objects may include different inanimate things
(e.g., fans, speakers, traffic, wind, rain, etc.) and animate
beings (e.g., people, animals). In one embodiment, the at least one
audio signal may include a plurality of audio signals from multiple
sound-emanating objects, each audio signal having a distinct tone,
distinct cadence, distinct loudness, or distinct combination of
tone, cadence, and loudness.
[0559] In step 4656, the processing device may determine that at
least one of the sounds was generated by a sound-emanating object
in the environment of the user, but outside of a field of view of
wearable camera 4402. The sound-emanating object may be outside of
the field of view of wearable camera 4402 when it generates sounds
and/or when the conditioned audio signals are being transmitted to
hearing interface device 4406. The processing device may determine
that at least one of the sounds was generated by a sound-emanating
object outside of the field of view of wearable camera 4402 by
identifying the objects in the field of view of wearable camera
4402 and determining that the at least one of the sounds was not
generated by any of the identified objects. The processing device
may also determine that at least one of the sounds was generated by
a sound-emanating object outside of the field of view of wearable
camera 4402 using information about objects in the field of view of
wearable camera 4402 (e.g., voiceprint, relationship, and more)
retrieved from a database, or objects that are not in the field of
view, but have been identified earlier in the field of view.
[0560] Consistent with the present disclosure, the processing
device may analyze the at least one audio signal to determine an
importance level of the sounds generated by a sound-emanating
object outside of the field of view of wearable camera 4402. In one
embodiment, the at least one sound may be associated with spoken
words, and the processing device may identify at least one of the
spoken words and determine an importance level of the at least one
sound based on the identity of at least one of the spoken words.
For example, the spoken words "help," "be careful," and the user's
name may be associated with a higher importance level than other
words. In another embodiment, the at least one sound generated by
the sound-emanating object may be associated with a frequency
range, and the processing device may determine an importance level
of the at least one sound based on the detected frequency range.
For example, a smoke alarm has a specific frequency and audio
signals with that specific frequency may be associated with a
higher importance level than other audio signals. For example, the
processing device may determine an importance level of a siren
based on context, e.g., a certain siren may be more important when
user 100 is walking in the street than when user 100 is
indoors.
[0561] In step 4658, the processing device may retrieve from a
database information associated with the at least one sound. The
database may be any device capable of storing information about one
or more sound-emanating objects, and may include a hard drive, a
solid-state drive, a web storage platform, a remote server, or the
like. The database may be located within apparatus 110 (e.g.,
within memory 550a) or external to apparatus 110 (e.g., within
memory 550b or within memory 4506). In some embodiments, the
database may be compiled by apparatus 110 through previous audio
analyses. For example, the processing device may store in the
database information associated with voices and sounds recognized
in audio signals captured by wearable microphone 4404. For example,
each time a voice detected in the audio signals is recognized as
complying with a stored voiceprint, the processing device stores
information associated with the detected sound-emanating object,
for example an updated voice print. The processing device may
retrieve information by analyzing the audio signals and identifying
the voiceprint of the sound-emanating object. The retrieved
information may include details associated with the identity of the
sound-emitting object. Specifically, in one embodiment, the
retrieved information may be indicative of a pre-existing
relationship of user 100 with the sound-emanating object, and the
at least one processor may be further programmed to determine an
importance level of the at least one sound based on the
pre-existing relationship. For example, the woman asking for help
in FIG. 44A may be the user's supervisor. In another embodiment,
the processing device may determine, based on analysis of the at
least one of the sounds, that the at least one of the sounds is
related to a public announcement. For example, the analysis of the
at least one of the sounds includes identifying a recognized word
or phrase associated with the public announcement. Moreover, the
processing device may determine the relevancy of the public
announcement to user 100 based on automatic review of calendar data
associated with user 100. The relevancy of the public announcement
to user 100 may affect the determination of the importance level.
For example, the processing device can access calendar data to
determine that the user is on flight 641 to X destination on a
certain day and time and selectively amplify announcements for this
flight.
[0562] In step 4660, the processing device may cause selective
conditioning of audio signals acquired by wearable microphone 4404
during the time period based on the retrieved information.
Consistent with the present disclosure, the conditioning may
include amplifying audio signals determined to correspond to the
sound-emanating object outside of the field of view of wearable
camera 4402 relative to other audio signals and/or optionally
attenuation or suppression of one or more audio signals associated
with a sound-emanating object inside the field of view of wearable
camera 4402. Additionally, or alternatively, selective conditioning
may include attenuation of audio signals determined to correspond
to the sound-emanating object outside of the field of view of
wearable camera 4402 relative to other audio signals and/or
optionally amplifying one or more audio signals associated with a
sound-emanating object inside the field of view of wearable camera
4402. Additionally, or alternatively, selective conditioning may
include changing a tone or rate of speech associated with the audio
signals determined to correspond to the sound-emanating object
outside of the field of view of wearable camera 4402 relative to
other audio signals to make the sound more perceptible to user 100
(e.g., increasing spaces between words, diction improvement, accent
improvement, and more). Various other processing may be performed
such as digitally reducing noise within the audio signal.
Consistent with the present disclosure, the processing device may
distinguish between three types of background noises. For example,
the first type may be a stationary noise that is substantially
constant over time, such as a refrigerator. The second type may be
nonstationary noise that is relatively transient, such as the sound
of a falling object. The third type may be temporary noise that is
longer in time than the second type and shorter in time than the
first type. Examples of the third type of background noise may
include a passing car, humming in an audience, and more. The
processing device may cause selective conditioning of audio signals
based on the identified type of background noise.
[0563] As described above, the processing device may determine,
based on the retrieved information, that an importance level of the
at least one sound is greater than a threshold. In this embodiment,
the selective conditioning of the audio signals may include
amplifying the at least one sound based on the determination of the
importance level. For example, the retrieved information may
identify some audio signals as a fire alarm and rank these audio
signals as important. When user 100 has a lower sensitivity to
tones in a range associated with the fire alarm, the selective
conditioning of the audio signals may include changing a tone of
the audio signals to make the fire alarm more perceptible to user
100. In another embodiment, the selective conditioning further
includes attenuating sounds generated by other sound-emanating
objects. The other sound-emanating objects may be inside or outside
the field of view of the camera.
[0564] In step 4662, the processing device may cause transmission
of the conditioned audio signals to hearing interface device 4406,
which may be configured to provide sounds to an ear of user 100.
Consistent with the present disclosure, the processing device may
cause a transmitter (e.g., wireless transceiver 530a) to transmit
the conditioned audio signals to hearing interface device 4406 via
a wireless network (e.g., cellular, Wi-Fi, Bluetooth.RTM., etc.),
or via near-field capacitive coupling, other short-range wireless
techniques, or via a wired connection. In addition, the processing
device may cause transmission of unprocessed audio signals together
with the conditioned audio signals to hearing interface device
4406. In one embodiment, the conditioned audio signals may be
transmitted to hearing interface device 4406 in less than 100 mSec
after the at least one audio signal was acquired by wearable
microphone 4404. For example, the conditioned audio signals may be
transmitted to hearing interface device 4406 in less than 50 mSec,
less than 30 mSec, less than 20 mSec, or less than 10 mSec after
the at least one audio signal was acquired by wearable microphone
4404.
[0565] Using Voice and Visual Signatures to Identify Objects
[0566] Consistent with the disclosed embodiments, a hearing aid
system may use voice and visual signatures to identify objects
within an environment of a user. The hearing aid system may analyze
captured images of the environment of a user to identify a
sound-emanating object and determine visual characteristics of the
object. When the identification is not certain (e.g., a confidence
level is below a predetermined level), or based on any other
criteria, the system may use a voiceprint determined from acquired
audio signals to identify the object. The hearing aid system may
repeat one or more portions of this process until a certainty
exceeds a threshold, and then take an action based on the
determined identity of the object. This association between visual
and audio identification provides for faster start of audio
analysis actions such as speaker separation.
[0567] FIG. 47A is a block diagram illustrating a hearing aid
system 4700 according to an example embodiment. Hearing aid system
4700 may include at least one wearable camera 4701, at least one
microphone 4702, at least one processor 4703, and at least one
memory 4704. Hearing aid system 4700 may further include additional
components beyond those shown in FIG. 47A. For example, hearing aid
system 4700 may include one or more components described above with
respect to FIGS. 5A-5C. Further, the components shown in FIG. 47A
may be housed in a single device or may be contained in one or more
different devices.
[0568] Wearable camera 4701 may be configured to capture one or
more images from the environment of user 100. In some embodiments,
wearable camera 4701 may be included in a wearable camera device,
such as apparatus 110. For example, wearable camera 4701 may be
camera 1730, as described above, which may also correspond to image
sensor 220.
[0569] Microphone 4702 may be configured to capture sounds from the
environment of user 100. In some embodiments, camera 4701 and
microphone 4702 may be included in the same device. Similar to
wearable camera 4701, microphone 4702 may be included in a wearable
camera device, such as apparatus 110. For example, apparatus 110
may comprise microphone 1720, as described with respect to FIG.
17B, which may be configured to determine a directionality of
sounds in the environment of user 100. As discussed above,
apparatus 110 may be worn by user 100 in various configurations,
including being physically connected to a shirt, necklace, a belt,
glasses, a wrist strap, a button, or other articles associated with
user 100. In some embodiments, one or more additional devices may
also be included, such as computing device 120. Accordingly, one or
more of the processes or functions described herein with respect to
apparatus 110 or processor 210 may be performed by computing device
120 and/or processor 540. Apparatus 110 may also communicate with a
hearing interface device worn by user 100, such as hearing
interface device 1710. Such communication may be through a wired
connection, or may be made wirelessly (e.g., using a Bluetooth.TM.,
NFC, or forms of wireless communication).
[0570] Processor 4703 may be configured to receive and process
images and audio signals captured by wearable camera 4701 and
microphone 4702. In some embodiments, processor 3803 may be
associated with apparatus 110, and thus may be included in the same
housing as wearable camera 4701 and microphone 4702. For example,
processor 4703 may correspond to processors 210, 210a or 210b, as
described above with respect to FIGS. 5A and 5B. In other
embodiments, processor 4703 may be included in one or more other
devices, such as computing device 120, server 250 (FIG. 2) or
various other devices. In such embodiments, processor 4703 may be
configured to receive data remotely, such as images captured by
wearable camera 4701 and audio signals captured by microphone
4702.
[0571] Memory 4704 may be configured to store information
associated with sound emanating objects in the environment of user
100. Memory 4704 may be any device capable of storing information
about one or more objects, and may include a hard drive, a solid
state drive, a web storage platform, a remote server, or the like.
Memory 4704 may be located within apparatus 110 (e.g., within
memory 550) or external to apparatus 110.
[0572] FIG. 47B is a schematic illustration showing an exemplary
environment for using voice and visual signatures to identify
objects consistent with the present disclosure. The environment of
user 100 may include one or more sound-emanating objects. The sound
emanating objects may include any objects capable of emitting
sounds that are perceptible to user 100 or apparatus 110. For
example, the sound emanating objects may be sound emanating objects
4710 and 4711, shown in FIG. 47. In some instances, sound emanating
objects 4710 or 4711 may be an individual, as shown in FIG. 47. In
other embodiments sound emanating objects 4710 or 4711 may be a
device, such as a radio, a speaker, a television, a mobile device
(e.g., a mobile phone, tablet, etc.), a computing device (e.g.,
personal computer, desktop computer, laptop, gaming console, etc.),
vehicles, alarms, or any other device capable of emitting sounds.
The sound-emanating objects 4710 or 4711 may also include other
objects, such as pets, animals, insects, natural features (e.g.,
streams, trees, etc.) or any other objects that may emanate
sounds.
[0573] Hearing aid system 4700 may be configured to receive images
and/or audio signals associated with sound emanating objects 4710
and/or 4711. For example, wearable camera 4701 may be included in
apparatus 110, worn by user 100. Wearable camera 4701 may capture
an image including a representation of sound emanating object 4710
within the environment of user 100. The image may contain
representations of other objects or features within the environment
of user 100. Processor 4703 may receive a plurality of images
captured by wearable camera 4701 and analyze the images to
determine visual characteristics of sound emanating object 4710.
Such visual characteristics may include any features of the object
represented in the image. For example, the visual characteristics
may include a color, shape, size, or the like. In some embodiments
the visual characteristics may be indicative of a type of the sound
emanating object. For example, the visual characteristics may
identify whether sound emanating object 4710 is an individual or an
inanimate object, a classification of the object (e.g., television,
vehicle, animal, person, etc.), an identity of an individual, an
identity of the object, or other similar object type
classifications. Accordingly, processor 4703 may use one or more
image recognition techniques or algorithms to detect features of
sound emanating object 4710. For example, processor 4703 may
identify one or more points, edges, vertices or other features of
the object. For example, where sound emanating object 4710 is an
individual, processor 4703 may further determine the visual
characteristics based on a facial analysis of an image of the
individual. Accordingly, processor 4703 may identify facial
features on the face of the individual, such as the eyes, nose,
cheekbones, jaw, or other features. Processor 4703 may use one or
more algorithms for analyzing the detected features, such as
principal component analysis (e.g., using eigenfaces), linear
discriminant analysis, elastic bunch graph matching (e.g., using
Fisherface), Local Binary Patterns Histograms (LBPH), Scale
Invariant Feature Transform (SIFT), Speed Up Robust Features
(SURF), or the like Similar feature recognition techniques may be
used for detecting features of inanimate objects as well.
[0574] Processor 4703 may further be configured to receive audio
signals associated with sound emanating objects in the environment
of user 100. The audio signals may be representative of one or more
sounds emanating from the sound emanating object. For example,
sound emanating object 4710 may emanate sound 4720, as shown in
FIG. 47B. Microphone 4702 may be configured to capture sound 4720
and convert it to an audio signal to be processed by processor
4703. Sound 4720 may be any sound or noise produced by sound
emanating object 4710. For example, sound 4720 may be an output of
a television, mobile phone, or other device, or a sound produced by
a vehicle. In instances where sound emanating object 4710 is an
individual, sound 4720 may be a voice of the individual. Processor
4703 may be configured to analyze the received audio signals to
determine a voiceprint of the sound emanating object. Processor
4703 may be configured to determine the voiceprint based on audio
analysis of a recording of the individual. This may be performed
using a voice recognition component, such as voice recognition
component 2041, as described in FIG. 20B. Processor 4703 may use
one or more voice recognition algorithms (e.g., Hidden Markov
Models, Dynamic Time Warping, neural networks, or other techniques)
to recognize the voice of the individual. The determined voiceprint
may include various characteristics associated with the individual,
such as an accent of the individual, the age of the individual, a
gender of the individual, or the like. While the voiceprint may
represent a voice pattern of an individual, the term voiceprint
should be interpreted broadly to include any sound pattern or
feature that may be used to identify sound emanating object
4710.
[0575] Memory 4704 may include one or more databases 4705
containing reference visual characteristics and reference
voiceprints corresponding to a plurality of objects. For example,
database 4705 may store a plurality of visual characteristics and
may associate one or more objects with the visual characteristics.
For example, database 4705 may associate a size, color, shape, or
other visual characteristics with a particular type of object, such
as a television or mobile phone. Database 4705 may also associate
visual characteristics with a specific object, rather than an
object type. For example, visual characteristics may be used to
identify a mobile phone or other object as belonging to user 100 or
another individual known to user 100. In some embodiments, database
4705 may include a list of contacts known to user 100. Visual
characteristics may include facial features used to identify a
particular individual. In some embodiments, database 4705 may be
associated with a social network platform, such as Facebook.TM.,
LinkedIn.TM., Instagram.TM., etc. Processor 4703 may be configured
to access database 4705 to identify sound emanating object 4710.
For example, processor 4703 may compare visual characteristics
determined from the captured images to visual characteristics
stored within database 4705. Processor 4703 may determine a match
between the sound emanating object represented in the images and an
object in database 4705 based on how closely the visual
characteristics match. In some embodiments, processor 4703 may
further be configured to determine a confidence score associated
with the match. For example, the confidence score may be based on
the number of visual characteristics detected in the image that
match visual characteristics in the database for a given object.
The confidence score may also be based on the degree to which the
visual characteristics match those in database 4705. For example,
if the visual characteristic is a color, the confidence score may
be based on how closely a color detected in the image matches a
color represented in database 4705. The confidence score may be
represented on a scale (e.g., ranging from 1-10, 1-100, etc.), as a
percentage or any other suitable format. In some embodiments,
identifying the object may comprise comparing the confidence score
to a certain threshold value, or determining a confidence score for
multiple potential objects and selecting the object with the
highest score.
[0576] Database 4705 may similarly associate voiceprint data with a
plurality of objects. For example, database 4705 may contain
voiceprint data associated with a number of individuals, similar to
the stored visual characteristic data described above. For example,
processor 4703 may compare voiceprint data determined from the
received audio signals to voiceprint data within database 4705.
Processor 4703 may determine a match between the sound emanating
object represented in the audio signals and an object in database
4705 based on how closely the voiceprint data matches. This process
may be used alone, or in conjunction with the visual characteristic
identification techniques described above. For example, sound
emanating object may be recognized using the visual characteristics
and may be confirmed using the voiceprint data, or vice versa. In
some embodiments the identification of the at least one sound
emanating object using the determined visual characteristics may
result in a group of candidate objects, and the identification of
the at least one sound emanating object may include selecting one
candidate of the group of candidate objects based on the
voiceprint.
[0577] Similar to the visual characteristics, processor 4703 may
further be configured to determine a confidence score associated
with the voiceprint match. For example, the confidence score may be
based on the degree to which the voiceprint detected in the audio
signals matches voiceprint data stored in database 4705 for a given
object. In some embodiments, the confidence score for the
voiceprint data may be combined with the confidence score based on
the visual characteristics, described above. For example, a single
confidence score may represent the degree of confidence that sound
emanating object 4710 corresponds with an object in database 4705
based on combined analysis of the visual characteristics and the
voiceprint. In some embodiments, processor 4703 may determine a
confidence score based on the visual characteristics and, if the
confidence score does not exceed a certain threshold, use the
voiceprint data to further identify sound emanating object 4710 and
refine the confidence score.
[0578] Consistent with the present disclosure, database 4703 may be
built at least in part through a machine learning process. For
example, database 4703 may be compiled by inputting a training data
set into a training algorithm to associate various visual
characteristics or voiceprints with known objects. Accordingly,
identifying the sound emanating object may be based on an output of
a trained neural network associated with database 4705. The trained
neural network may be continuously improved as hearing aid system
4700 continues to identify objects. For example, user 100 may
confirm or manually edit the identity of objects identified by
processor 4703 and the neural network may be adjusted or further
developed based on the feedback from user 100. Such feedback may be
received through a device associated with user 100, such as
apparatus 110, computing device 120, or any other device capable of
interacting with hearing aid system 4700 (e.g., through a network
connection, etc.).
[0579] In some embodiments, processor 4703 may be configured to
determine the identity of a sound emanating object based on visual
characteristics or voiceprint data associated with another sound
emanating object. For example, the at least one sound emanating
object may include a first sound emanating object (e.g., sound
emanating object 4710) and a second sound emanating object (e.g.,
sound emanating object 4711). Hearing aid system 4700 may use
determined visual characteristics of the first sound emanating
object to identify the second sound emanating object. Similarly,
hearing aid system 4700 may use the determined voiceprint of the
first sound emanating object to identify the second sound emanating
object. The visual characteristics or voiceprint data from first
sound emanating object 4710 may be indicative of the identity of
second sound emanating object 4711. For example, where the sound
emanating objects are individuals, a first individual may
frequently be encountered along with the second individual. As
another example, an individual may frequently be associated with an
object such as a mobile phone, pet, or the like. Processor 4703 may
determine the identity of the object based on visual
characteristics (e.g., face recognition) and voiceprint data
associated with the individual. Accordingly, database 4705 (or
memory 4704) may be configured to store associations between
various objects within the database.
[0580] Processor 4703 may be configured to adjust the confidence
score based on whether second sound emanating object 4711 was
identified based on visual characteristics and/or voiceprint data
of first sound emanating object 4710. For example, where second
emanating object 4711 was identified based on visual
characteristics and/or voiceprint data of first sound emanating
object 4710 alone, processor 4703 may assign a lower confidence
score. One the other hand, where second sound emanating object 4711
was identified based on visual characteristics and/or voiceprint
data associated with second sound emanating object 4711 and
confirmed using visual characteristics and/or voiceprint data
associated with first sound emanating object 4710, processor 4703
may assign a higher confidence score than if second sound emanating
object 4711 was identified based on its own visual characteristics
and/or voiceprint alone.
[0581] In some embodiments, hearing aid system 4700 may be
configured to perform various actions based on identifying sound
emanating object 4710. In some embodiments, processor 4703 may
store information relating to the identification of 4710. For
example, processor 4703 may store in memory 4704 information
relating to an encounter with sound emanating object 4710. This may
include storing information such as the identity of the object (or
identity of an individual) determined above. The information may
further include a time associated with the identification, a time
associated with the image or audio signal being captured, a
location (e.g., of user 100 or of sound emanating object 4710),
data associated with the sound emanating object (e.g., the captured
images or audio signals, etc.), keywords mentioned in an encounter,
or various other information. In some embodiments, processor 4703
may maintain a timeline of identified objects or other events
associated with apparatus 110, and processor 4703 may add the
identified sound emanating object to the timeline. In some
embodiments, storing the information may include updating database
4705. For example, the information may be used for updating the
visual characteristics of sound emanating object 4710 or may be
used for updating the voiceprint of sound emanating object 4710.
The stored information may improve the accuracy of the associations
stored in database 4705 and thereby improve the accuracy of hearing
aid system 4700 in future object identification.
[0582] In some embodiments, hearing aid system 4700 may be
configured to condition the sound received from the sound emanating
object. In some embodiments, the action performed by hearing aid
system 4700 may include causing selective conditioning of at least
one audio signal associated with the at least one sound emanating
object and causing transmission of the at least one conditioned
audio signal to a hearing interface device configured to provide
sounds to an ear of the user. For example, processor 4703 may
receive an audio signal associated with sound 4720 from sound
emanating object 4710. Based on the identification of sound
emanating object 4710, processor 4703 may selectively condition the
audio signal associated with sound 4720. For example, sound
emanating object 4710 may be a television and processor 4703 may
selectively condition the audio of the television. Where sound
emanating object 4710 is an individual, processor 4703 may
determine that sound 4720 emanating from the individual should be
selectively conditioned.
[0583] In some embodiments, conditioning may include changing a
tone of one or more audio signals corresponding to sound 4720 to
make the sound more perceptible to user 100. For example, user 100
may have lesser sensitivity to tones in a certain range and
conditioning of the audio signals may adjust the pitch of sound
4720. For example, user 100 may experience hearing loss in
frequencies above 10 kHz and processor 210 may remap higher
frequencies (e.g., at 15 kHz) to 10 kHz. In some embodiments
processor 210 may be configured to change a rate of speech
associated with one or more audio signals. Processor 210 may be
configured to vary the rate of speech of sound emanating object
4710 to make the detected speech more perceptible to user 100. The
type and degree of selective conditioning may depend on the
particular object or individual that was identified and/or on
preferences of the user. For example, memory 4704 (e.g., database
4705) may store selective conditioning functions associated with
particular objects.
[0584] In some embodiments, selective conditioning may include
attenuation or suppressing one or more audio signals not associated
with sound emanating object 4710, such as sounds 4721 and 4722,
which may emanate from other objects within the environment (e.g.,
sound emanating object 4711), or may be background noise. Similar
to amplification of sound 4720, attenuation of sounds may occur
through processing audio signals, or by varying one or more
parameters associated with microphone 4702 to direct focus away
from sounds not associated with sound emanating object 4710.
[0585] Where more than one sound emanating object is detected,
hearing aid system 4700 may selectively condition sounds associated
with the sound emanating objects relative to each other. For
example, the at least one sound emanating object may include a
first sound emanating object (e.g., sound emanating object 4710)
and a second sound emanating object (e.g., sound emanating object
4711). Selective conditioning may include attenuating a first audio
signal associated with the first sound emanating object; and
amplifying a second audio signal associated with the second sound
emanating object. Similarly, selective conditioning may include
changing a tone of a first audio signal associated with the first
sound emanating object; and avoiding from changing a tone of a
second audio signal associated with the second sound emanating
object. Accordingly, the audio signal associated with the first
sound emanating object may be more perceptible to user 100. Where
the sound emanating objects are individuals, selective conditioning
may include changing a rate of speech associated with the first
individual and avoid from changing a rate of speech associated with
the second individual. For example, processor 4703 may add short
pauses between words associated with the first individual in order
to make the audio more intelligible. Various other forms of
selective conditioning may also be performed to improve the
presentation of the audio signal to user 100.
[0586] Hearing aid system 4700 may perform other actions, such as
presenting the determined identity of sound emanating object 4710
to user 100. The identity may be presented in various ways. In some
embodiments, hearing aid system 4700 may audibly present the
identification of the object to the user, for example, through
hearing interface device 1710, computing device 120, or various
other devices. Hearing aid system 4700 may read the name of the
detected object to the user. Accordingly, hearing aid system may
access one or more speech-to-text algorithms or software components
for presenting a name of an object in database 4705. In other
embodiments, prerecorded names of the objects may be stored in
memory 4704. Where the sound emanating object is an individual,
hearing aid system 4700 may present the name of the individual to
the user and/or other information associated with the individual
(e.g., a relationship to the individual, an age of the individual,
names of other individuals associated with the individual, a title
of the individual, etc.).
[0587] Hearing aid system 4700 may also present the determined
identity of sound emanating object 4710 to user 100 visually. FIG.
48 is an illustration showing an exemplary device displaying the
name of a sound emanating object consistent with the present
disclosure. As shown in FIG. 48, hearing aid system 4700 may
display information about sound emanating object 4710 on a display
of device 4801. In some embodiments, device 4801 may be a paired
wearable device, such as a mobile phone, tablet, personal computer,
smart watch, heads up display (HUD), or the like. In embodiments
where sound emanating device 4710 is an individual, the at least
one action performed by hearing aid system 4700 may include causing
a name 4810 of the individual to be shown on the display. Various
other information may also be presented on the display. For
example, device 4801 may display an image 4811 of the object or
individual, as shown in FIG. 48. Where sound emanating object is an
individual, hearing aid system 4700 may display various other
identification information associated with the individual (e.g., a
phone number, address, title, company, relationship, age, etc.).
The display may also include other functionality associated with
the individual, such as contacting the individual (e.g., by phone,
email, SMS, etc.), access an account associated with the individual
(e.g., a social media page, file sharing account or location,
etc.), or the like. In some instances, the display may also include
functionality for confirming or editing the identification of sound
emanating object 4710, for example, to improve a trained neural
network or other machine learning system, as described above.
[0588] FIG. 49 is a flowchart showing an exemplary process 4900 for
using voice and visual signatures to identify objects consistent
with disclosed embodiments. Process 4900 may be performed by
hearing aid system 4700, for example by processor 4703. As
described above, processor 4703 may correspond to one or more other
processors described in detail above, including processors 210,
210a and/or 210b. Accordingly, process 4900 may be performed by a
processor associated with a wearable camera device, such as
apparatus 110. Some or all of process 4900 may be performed by
processors associated with other components, such as computer
device 120, server 250, or other devices. As described above,
hearing aid system 4900 may include memory 4704 configured to store
a database (e.g., database 4705) of reference visual
characteristics and reference voiceprints corresponding to a
plurality of objects. Processor 4703, or the processor performing
various steps of process 4900 may access memory 4704.
[0589] In step 4910, process 4900 may include receiving image data
and audio signals associated with at least one sound emanating
object. For example, step 4910 may include receiving a plurality of
images captured by a wearable camera, wherein at least one of the
plurality of images depicts at least one sound emanating object in
an environment of a user. The images may be captured, for example,
by wearable camera 4701 and may include a representation of sound
emanating object 4710. The images may be received by processor
4703. Step 4910 may further include receiving audio signals
acquired by a wearable microphone, wherein the audio signals are
representative of one or more sounds emanating from the at least
one sound emanating object. For example, processor 4703 may receive
audio signals from microphone 4702 which may represent sound 4720
emanating from sound emanating object 4710. The audio signals may
be received concurrently with the captured images, or may be
received later during process 4900, for example, after an
identification of sound emanating object 4710 has been made based
on the captured images.
[0590] In step 4920, process 4900 may include analyzing at least
one of the received plurality of images to determine one or more
visual characteristics associated with the at least one sound
emanating object. For example, processor 4703 may use one or more
image recognition techniques to extract features from the image
that are associated with sound emanating object 4710. The extracted
features may be analyzed to determine the visual characteristics,
which may include a color, shape, arrangement, size, or other
characteristic of the object. The visual characteristics may be
indicative of the type of an object, such as whether the object is
an individual or an inanimate object, a classification of the
object, etc. In some instances, sound emanating object 4710 may be
an individual. Accordingly, step 4920 may include determining the
visual characteristics based on a facial analysis of an image of
the individual. Accordingly, processor 4703 may identify facial
features on the face of the individual, such as the eyes, nose,
cheekbones, jaw, or other features. Processor 4703 may use one or
more algorithms for analyzing the detected features, such as
principal component analysis (e.g., using eigenfaces), linear
discriminant analysis, elastic bunch graph matching (e.g., using
Fisherface), Local Binary Patterns Histograms (LBPH), Scale
Invariant Feature Transform (SIFT), Speed Up Robust Features
(SURF), or the like.
[0591] In step 4930, process 4900 may include identifying (or
attempting to identify) within the database in view of the one or
more visual characteristics, the at least one sound emanating
object and determine a degree of certainty of identification.
Accordingly, process 4900 may further include accessing a database
of reference visual signatures and reference voice signatures
corresponding to a plurality of objects. As described above,
processor 4703 may access database 4705, which may store a
plurality of visual characteristics, a plurality of objects, and
associations between the visual characteristics and the objects.
Processor 4703 may attempt to match the visual characteristics
determined in step 4920 to visual characteristics within database
4705. In some embodiments, as described above, the at least one
sound emanating object may include a first sound emanating object
and a second sound emanating object, and step 4930 may further
comprise using determined visual characteristics of the first sound
emanating object to identify the second sound emanating object.
Processor 4703 may determine a confidence score corresponding to a
degree of certainty that the sound emanating object represented in
the captured images corresponds to one or more objects in database
4705. In some embodiments, step 4730 may include generating a
confidence score for more than one object in database 4705 and
identifying sound emanating object 4710 as the object in database
4710 corresponding to the highest confidence score.
[0592] In some instances, the at least one sound emanating object
may be identified based on the visual characteristics alone. In
some instances, however, process 4900 may further include
identifying the at least one sound emanating object based on audio
signals associated with the sound emanating object(s). Accordingly,
process 4900 may include a step 4935 of determining whether
identification based on the visual characteristics is sufficient.
For example, step 4935 may comprise comparing the confidence score
determine in step 4930 with a certain threshold. Where the
confidence scores are represented as a percentage (with 100%
representing a maximum confidence), for example, the threshold may
be an intermediate value (e.g. 40%, 50%, 60%, 70%, etc.). The
threshold may be higher or lower depending on the use of the
system. In some embodiments the threshold may vary based on various
other factors or settings, for example, based on the type of object
identified, an image quality, an importance value associated with
correctly identifying the object, a time of day, a threshold set by
a user, a threshold set by an administrator, etc.). If the
confidence score exceeds the threshold, process 4900 may proceed to
step 4960, as indicated in FIG. 49. If the confidence score is
below the threshold, however, process 4900 may proceed to step
4940. The outcome of step 4935 may be determined by other factors
besides the confidence score. For example, a user or administrator
may change a setting to always proceed to step 4960 or 4940. In
other embodiments the determination may be based on other factors,
such as a type of the sound emanating object (e.g., whether the
object is an individual, etc.) or an importance value (e.g., if
hearing aid system is identifying an oncoming vehicle, etc.).
[0593] In step 4940, process 4900 may include analyzing received
audio signals to determine a voiceprint of the at least one sound
emanating object. As discussed above, with respect to step 4910,
step 4940 may include a step of receiving audio signals acquired by
a wearable microphone if the audio signals have not yet been
received. The audio signals may be representative of one or more
sounds emanating from the at least one sound emanating object.
Processor 4703 may analyze the received audio signals to identify a
voiceprint of the sound emanating object. In instances where the at
least one sound emanating object is an individual, step 4940 may
include determining the voiceprint based on audio analysis of a
recording of the individual. For example, processor 4703 may use
one or more voice recognition algorithms, such as Hidden Markov
Models, Dynamic Time Warping, neural networks, or other techniques,
to recognize the voice of the individual. The determined voiceprint
may include characteristics of the individual, such as an accent,
age, gender, vocabulary, or the like.
[0594] In step 4950, process 4900 may include identifying the at
least one sound emanating object based on the visual
characteristics and the determined voiceprint. For example,
processor 4703 may access database 4705, which may store voiceprint
data associated with a plurality of objects. Processor 4703 may be
configured to determine a match between the voiceprint determined
in step 4940 and the voiceprint data stored in database 4705. In
some embodiments the identification of the at least one sound
emanating object using the determined visual characteristics (e.g.,
in step 4930) results in a group of candidate objects, and the
identification of the at least one sound emanating object includes
selecting one of the group of candidate objects based on the
voiceprint. In other embodiments, the voiceprint data may be used
to identify candidate objects independently and compare the
candidate objects to those identified in step 4930. In some
embodiments, as described above, the at least one sound emanating
object may include a first sound emanating object and a second
sound emanating object, and step 4930 may further comprise using
determined visual characteristics of the first sound emanating
object to identify the second sound emanating object. Step 4950 may
further include determining a confidence score associated with the
identification based on the voiceprint. In some embodiments, the
confidence score may be cumulative, representing a confidence based
on both the visual characteristic identification in step 4930 and
the voiceprint identification in step 4950. In other embodiments, a
voiceprint confidence score may be determined separately.
[0595] In step 4955, process 4950 may include reassessing the
identification of the at least one sound emitting object. Similar
to step 4935, step 4955 may comprise comparing the confidence score
from step 4950 with a predetermined threshold. Threshold may be the
same threshold described above with reference to step 4935 or may
be a different threshold. For example, a confidence score based on
a combined analysis under steps 4930 and 4950 may be subject to a
higher confidence score threshold than based on step 4930 alone.
The threshold value and the determination under step 4955 generally
may be based on other factors as described above with respect to
step 4935. If the confidence score exceeds the threshold, process
4900 may proceed to step 4960. If the confidence score does not
meet the threshold value, however, process 4900 may return to step
4910. For example, hearing aid system 4700 may determine that the
object cannot be identified based on the received images and audio
signals and may obtain additional images and/or audio signals to
complete the identification. Process 4900 may include other steps,
such as sending a notification to a user indicating the
identification failed, or the like.
[0596] In step 4960, process 4900 may comprise initiating at least
one action based on an identity of the at least one sound emanating
object. As described above, the at least one action may include
causing selective conditioning of at least one audio signal
associated with the at least one sound emanating object. The at
least one action may further include causing transmission of the at
least one conditioned audio signal to a hearing interface device
configured to provide sounds to an ear of the user, such as hearing
interface device 1710. For example, the selective conditioning may
include varying a tone, volume, or rate of speech of the audio
signal, as discussed in greater detail above. In some instances,
the at least one sound emanating object includes a first sound
emanating object and a second sound emanating object, and causing
selective conditioning of the at least one audio signal may include
attenuating a first audio signal associated with the first sound
emanating object and amplifying a second audio signal associated
with the second sound emanating object. The selective conditioning
may further include changing a tone of a first audio signal
associated with the first sound emanating object and avoiding
hanging a tone of a second audio signal associated with the second
sound emanating object. In some instances, the at least one sound
emanating object may include a first individual and a second
individual, and causing selective conditioning of the at least one
audio signal may include changing a rate of speech associated with
the first individual and avoiding changing a rate of speech
associated with the second individual.
[0597] In some embodiments, the at least one action may include
storing in the at least one memory device information relating to
an encounter with the at least one sound emanating object, as
described in greater detail above. The stored information may be
used for updating the visual characteristics and/or voiceprint of
the at least one sound emanating object in database 4705. For
example, the stored information may be used to ensure that database
4705 is accurate and/or up to date, as discussed in greater detail
above.
[0598] In some embodiments, where the at least one sound emanating
object (e.g., sound emanating object 4710) is an individual, the at
least one action may include causing a name of the individual to be
shown on a display, as discussed above in reference to FIG. 48. The
display may be associated with a paired wearable device (e.g.,
device 4801), such as a mobile phone, smartwatch, or other mobile
device. Other information or functionality may also be displayed
for user 100, as discussed in detail above.
[0599] Selective Input for a Hearing Aid Based on Image Data
[0600] Consistent with the disclosed embodiments, a hearing aid
system may selectively condition audio signals from sound emanating
objects within the environment of a user. The hearing aid system
may access a database storing information about various sound
emanating objects and may selectively condition audio from the
sound emanating objects based on the information stored in the
database. As one example, the hearing aid system may determine a
relative rank or importance of the various sound emanating objects
and selectively condition audio signals associated with the sound
emanating objects based on the relative rank or importance. The
hearing aid system may also selectively condition audio signals
from the sound emanating objects based on the context, for example
based on the location of the user.
[0601] The hearing aid system of the present disclosure may
correspond to hearing aid system 4700, described above with respect
to FIG. 47A. For example, the hearing aid system may include at
least one wearable camera 4701, at least one microphone 4702, at
least one processor 4703, and at least one memory 4704. While the
hearing aid system for selectively condition audio signals from
sound emanating objects is described in reference to hearing aid
system 4700 throughout the present disclosure, it is understood
that the hearing aid system may be separate and/or different from
hearing aid system 4700. For example, the hearing aid system may
include additional or fewer components than those shown in FIG.
47A. Further, as discussed above, the components shown in FIG. 47A
may be housed in a single device or may be contained in one or more
different devices.
[0602] As discussed above, wearable camera 4701 may be configured
to capture one or more images from the environment of user 100. In
some embodiments, wearable camera 4701 may be included in a
wearable camera device, such as apparatus 110. For example,
wearable camera 4701 may be camera 1730, as described above, which
may also correspond to image sensor 220. Microphone 4702 may be
configured to capture sounds from the environment of user 100. In
some embodiments, camera 4701 and microphone 4702 may be included
in the same device. Microphone 4702 may be included in a wearable
camera device, such as apparatus 110. For example, apparatus 110
may comprise microphone 1720, as described with respect to FIG.
17B, which may be configured to determine a directionality of
sounds in the environment of user 100. Apparatus 110 may be worn by
user 100 in various configurations, including being physically
connected to a shirt, necklace, a belt, glasses, a wrist strap, a
button, or other articles associated with user 100. In some
embodiments, one or more additional devices may also be included,
such as computing device 120. Accordingly, one or more of the
processes or functions described herein with respect to hearing aid
system 4700 or processor 4703 may be performed by computing device
120 and/or processor 540.
[0603] Processor 4703 may be configured to receive and process
images and audio signals captured by wearable camera 4701 and
microphone 4702. As discussed above, processor 4703 may be
associated with apparatus 110, and thus may be included in the same
housing as wearable camera 4701 and microphone 4702. For example,
processor 4703 may correspond to processors 210, 210a or 210b, as
described above with respect to FIGS. 5A and 5B. In other
embodiments, processor 4703 may be included in one or more other
devices, such as computing device 120, server 250 (FIG. 2) or
various other devices. In such embodiments, processor 4703 may be
configured to receive data remotely, such as images captured by
wearable camera 4701 and audio signals captured by microphone
4702.
[0604] Memory 4704 may be configured to store information
associated with sound emanating objects in the environment of user
100. Memory 4704 may be any device capable of storing information
about one or more objects, and may include a hard drive, a solid
state drive, a web storage platform, a remote server, or the like.
Memory 4704 may be located within apparatus 110 (e.g., within
memory 550) or external to apparatus 110. In some embodiments,
memory 4704 may further include a database, such as database 5020,
which is described in detail below.
[0605] Apparatus 110 may also communicate with a hearing interface
device worn by user 100. For example, the hearing aid device may be
hearing interface device 1710, as shown in FIG. 17A. As described
above, hearing interface device 1710 may be any device configured
to provide audible feedback to user 100. Hearing interface device
1710 may be placed in one or both ears of user 100, similar to
traditional hearing interface devices. Hearing interface device
1710 may be of various styles, including in-the-canal,
completely-in-canal, in-the-ear, behind-the-ear, on-the-ear,
receiver-in-canal, open fit, or various other styles. Hearing
interface device 1710 may include one or more speakers for
providing audible feedback to user 100, a communication unit for
receiving signals from another system, such as apparatus 110,
microphones for detecting sounds in the environment of user 100,
internal electronics, processors, memories, etc. Hearing interface
device 1710 may correspond to feedback outputting unit 230 or may
be separate from feedback outputting unit 230 and may be configured
to receive signals from feedback outputting unit 230.
[0606] In some embodiments, hearing interface device 1710 may
comprise a bone conduction headphone 1711, as shown in FIG. 17A.
Bone conduction headphone 1711 may be surgically implanted and may
provide audible feedback to user 100 through bone conduction of
sound vibrations to the inner ear. Hearing interface device 1710
may also comprise one or more headphones (e.g., wireless
headphones, over-ear headphones, etc.) or a portable speaker
carried or worn by user 100. In some embodiments, hearing interface
device 1710 may be integrated into other devices, such as a
Bluetooth.TM. headset of the user, glasses, a helmet (e.g.,
motorcycle helmets, bicycle helmets, etc.), a hat, etc. Hearing
interface device 1710 may be configured to communicate with a
camera device, such as apparatus 110. Such communication may be
through a wired connection, or may be made wirelessly (e.g., using
a Bluetooth.TM., NFC, or forms of wireless communication).
Accordingly, hearing interface device 1710 may include a receiver
configured to receive at least one audio signal and an
electroacoustic transducer configured to provide sounds from the at
least one audio signal to an ear of the user.
[0607] FIG. 50A is a schematic illustration showing examples of
sound emitting objects that may be identified in an environment
5000 of a user consistent with the present disclosure. As discussed
above, the sound emanating objects may include any objects capable
of emitting sounds that are perceptible to user 100 or apparatus
110. In some instances, sound emanating objects may be a person,
such as individuals 5010 and 5011 shown in FIG. 50A. In other
embodiments the sound emanating objects may be a device, such as
television 5012, shown in FIG. 50A. Sound emanating objects may
include other devices, such as a radio, a speaker, a television, a
mobile device (e.g., a mobile phone, tablet, etc.), a computing
device (e.g., personal computer, desktop computer, laptop, gaming
console, etc.), vehicles, alarms, or any other device capable of
emitting sounds. Sound emanating objects may also include other
objects, such as pets, animals, insects, natural features (e.g.,
streams, trees, etc.), inanimate objects, weather-related objects,
or any other objects or portions of an object that may emanate
sounds.
[0608] FIG. 50B is an illustration of an example database 5020
storing information associated with sound emanating objects
consistent with the present disclosure. Database 5020 may be
maintained on any memory associated with hearing aid system 4700,
such as memory 4704. Database 4705 may correspond to database 4705,
described above, or may be a separate database. In some
embodiments, database 5020 may be located separately from hearing
aid system 4700, for example on a remote device or server, and may
be accessible by hearing aid system 4700. As shown in FIG. 50B,
database 5020 may store visual characteristics of one or more sound
emanating objects. The visual characteristics may include features
or attributes of the sound emanating objects that may be detected
by hearing aid system 4700. For example, the visual characteristics
may include a size, color, shape, pattern, or other visual features
of the associated sound emanating object. Visual characteristics
may include facial features used to identify a particular
individual.
[0609] Database 5020 may include other information about the sound
emanating object, such as a name, type, relationship, level of
importance, voiceprint data, and/or rules for audio conditioning.
Where the sound emanating object is an individual, the name of the
sound emanating object may be associated with the individual's
name. The relationship stored in database 5020 may define a
relationship between the individual and user 100, such as whether
the individual is a friend, colleague, family relative,
acquaintance, or any other forms of relationships that may be
defined. For example, as shown in FIG. 50B, individual Cindy Moore
may be a colleague of user 100, where individual Raj Polar may be a
friend of user 100. In some embodiments, more specific
relationships may be defined, such as identifying a co-worker as a
manager of the user, identifying a family member as the user's
father, identifying a friend as close friend, etc. In some
embodiments, database 5020 may be associated with a list of
contacts of user 100, a social network platform (e.g.,
Facebook.TM., LinkedIn.TM., Instagram.TM., etc.), or various other
associated lists or databases, and may be configured to determine a
relationship based on data received from the lists or
databases.
[0610] The sound emanating object may also be a device or other
object, as described above. In some instances, the name of the
sound emanating object may be a generic name of the device (e.g.,
laptop, television, phone, etc.) In some embodiments, hearing aid
system 4700 may recognize a particular device, rather than just a
general device type. Accordingly, the name of the sound emanating
object stored in database 5020 may be specific to the detected
device. For example, the name may identify the owner of the device,
(e.g., "my phone," "Terri's laptop," etc.). In some embodiments,
the name may also include a serial number or other unique
identifier of the device. Similarly, the relationship of the sound
emanating object may indicate whether the sound emanating object is
associated with user 100 in some way.
[0611] Database 5020 may further store information pertaining to
selective audio conditioning of the sound emanating object. For
example, the level of importance may rank the sound emanating
objects in database 5020 relative to each other. In some
embodiments, each device may be uniquely ranked relative to each of
the other sound emanating objects in the database. In other
embodiments, the sound emanating objects may be ranked on a scale
(e.g., 1-5, 1-10, 1-100, etc.), as a percentage, based on
predefined ranking levels (e.g., "high importance," "low
importance," etc.) or any other suitable ranking method. In some
embodiments, the ranking may be based on the relationship to the
user. For example, family members of user 100 may be given a higher
importance ranking than acquaintances of user 100. Similarly, a
manager or boss of user 100 may be given a higher importance
ranking than a peer of user 100. Database 5020 may also store
specific audio conditioning rules associated with the sound
emanating object. For example, as shown in FIG. 50B, the rules may
include a predefined conditioning parameter to be applied to an
audio signal associated with the sound emanating object, such as
changing a pitch or volume of the audio signal. The conditioning
parameter may be absolute (e.g., a set volume level, +10% volume,
etc.) or may be defined relative to other sounds in the environment
(e.g., increase volume relative to other sounds). In some
embodiments, the rule may be associated with one or more other
parameters, such as the relationship to the user. For example, the
rule may apply to all family members of user 100 or may apply to
individuals of a certain level of importance. In some embodiments,
the rules may further include context-based conditions, for
example, based on current or previous actions of user 100, the
environment of user 100 or any other context-based rules. Referring
to the examples shown in FIG. 50B, hearing aid system 4700 may be
configured to mute a television when user 100 is not looking at it
or increase the volume of an individual when they are meeting
outside. Accordingly, hearing aid system 4700 may be configured to
determine an environment of the user, for example, based on
analyzing other objects in captured images, analyzing captured
audio, using global positioning system (GPS) data, or the like.
Other audio conditioning methods are described in greater detail
below.
[0612] In some embodiments, database 5020 may also store voiceprint
data associated with sound emanating objects. The voiceprint data
may be unique to the particular sound emanating object that it is
associated with (similar to the voiceprint data in database 4705,
described above). Accordingly, the voiceprint may be suitable for
identifying the sound emanating object. For example, processor 4703
may identify a sound emanating object, such as an individual,
through the visual characteristics described above and may retrieve
information associated with the sound emanating object from
database 5020. In some embodiments, the information may include the
voiceprint, which may be used for further identifying the sound
emanating object, or the like. In some instances, the voiceprint
information for a particular sound emanating object may include a
set of reference voiceprints. For example, a first voiceprint of a
specific individual may be associated with a scenario where the
specific individual is standing next to the user, and a second
voiceprint of the specific individual may be associated with a
scenario where the specific individual is talking through a
communication device.
[0613] In some embodiments, the information stored in database 5020
may be designated and/or modified by user 100 or another individual
(e.g., a caretaker, administrator, etc.). For example, user 100 may
manually add to or edit the sound emanating objects in database
5020, for example through a user interface, such as computing
device 120. User 100 may define the name of the sound emanating
object, the classification type, the relationships, the level of
importance, and/or the rules for audio conditioning. In some
embodiments, database 5020 may be built and/or modified through an
automated process. For example, hearing aid system 4700 may be
configured to learn one or more properties or values associated
with a sound emanating object based on the interaction of user 100
with the sound emanating object. If user 100 continually increases
the volume of hearing interface device 1710 when interacting with a
particular sound emanating object, hearing aid system 4700 may
automatically include a rule to increase the volume of audio
signals associated with that sound emanating object. As another
example, user 100 may more frequently look at a particular sound
emanating object relative to other sound emanating objects and
hearing aid system 4700 may assign a level of importance,
relationship, rule for audio conditioning, or another property
based on the behavior of user 100.
[0614] FIG. 51A is a schematic illustration showing an example
environment 5100 for selectively conditioning audio signals
consistent with the present disclosure. Environment 5100 of user
100 may include one or more sound emanating objects, as discussed
above. For example, environment 5100 may include sound emanating
objects 5110 and 5111, which may be individuals, and sound
emanating object 5512, which may be a device.
[0615] Hearing aid system 4700 may be configured to receive images
and/or audio signals associated with sound emanating objects 5110,
5111, and 5112. For example, wearable camera 4701 may be included
in apparatus 110, worn by user 100. Wearable camera 4701 may
capture an image including a representation of sound emanating
object 5110 within the environment of user 100. Processor 4703 may
receive a plurality of images captured by wearable camera 4701 and
analyze the images to determine visual characteristics of sound
emanating object 5110. Such visual characteristics may include any
features of the object represented in the image. For example, the
visual characteristics may include a color, shape, size, type, or
the like, which may correspond to the visual characteristic types
stored in database 5020. Accordingly, processor 4703 may use one or
more image recognition techniques or algorithms to detect features
of sound emanating object 5110. For example, processor 4703 may
identify one or more points, edges, vertices or other features of
the object. Where sound emanating object 5110 is an individual,
processor 4703 may further determine the visual characteristics
based on a facial analysis or face recognition of an image of the
individual. Accordingly, processor 4703 may use one or more
algorithms for analyzing the detected features, such as principal
component analysis (e.g., using eigenfaces), linear discriminant
analysis, elastic bunch graph matching (e.g., using Fisherface),
Local Binary Patterns Histograms (LBPH), Scale Invariant Feature
Transform (SIFT), Speed Up Robust Features (SURF), or the like.
Similar feature recognition techniques may be used for detecting
features of inanimate objects as well, such as sound emanating
object 5112.
[0616] In addition to identifying sound emanating objects, hearing
aid system 4700 may also determine a context of the sound emanating
objects. Accordingly, processor 4703 may be configured to analyze
other features or objects within the captured images. For example,
objects such as trees, flowers, grass, buildings, etc. may indicate
that user 100 is outside. Other objects, such as chairs, desks,
computer screens, printers, etc. may indicate that user 100 is in
an office environment. In some embodiments, processor 4703 may
associate particular objects or groups of objects with a particular
environment of user 100. For example, processor 4703 may recognize
one or more objects to determine that user 100 is in a particular
room, such as a living room of user 100, a particular office or
conference room, etc. This contextual information may be used for
selectively conditioning audio signals associated with a sound
emanating object, as described in further detail below.
[0617] Processor 4703 may further be configured to receive audio
signals associated with sound emanating objects in the environment
of user 100. The audio signals may be representative of one or more
sounds emanating from the sound emanating object. For example,
sound emanating objects 5110, 5111, and 5112 may emanate sounds
5120, 5121, and 5122, respectively, as shown in FIG. 51A. Hearing
aid system 4700 may be configured to capture sounds 5120, 5121, and
5122 (e.g., through microphone 4702) and convert them to an audio
signal to be processed by processor 4703. In instances where the
sound emanating object is an individual, such as sound emanating
object 5110, sound 5120 may be a voice of the individual. Where the
sound emanating object is a device or other object, such as sound
emanating object 5112, sound 5122 may be an output of the device,
such as sound from a television, mobile phone, or other device, a
sound produced by a vehicle, etc.
[0618] In some embodiments, processor 4703 may be configured to
determine a voiceprint of the sound emanating object. The
voiceprint may be determined according to any of the methods
discussed above with respect to FIG. 47B. For example, processor
4703 may use one or more voice recognition algorithms (e.g., Hidden
Markov Models, Dynamic Time Warping, neural networks, or other
techniques) to recognize the voice of the individual. The
determined voiceprint may include various characteristics
associated with the individual, such as an accent of the
individual, the age of the individual, a gender of the individual,
or the like. While the voiceprint may represent a voice pattern of
an individual, the term voiceprint should be interpreted broadly to
include any sound pattern or feature that may be used to identify a
sound emanating object.
[0619] Hearing aid system 4700 may be configured to selectively
condition the sound received from one or more sound emanating
objects. In some embodiments, conditioning may include changing a
tone of one or more audio signals corresponding to sound 5120 to
make the sound more perceptible to user 100. User 100 may have
lesser sensitivity to tones in a certain range and conditioning of
the audio signals may adjust the pitch of sound 5120. For example,
user 100 may experience hearing loss in frequencies above 10 kHz
and processor 4703 may remap higher frequencies (e.g., at 15 kHz)
to 10 kHz. In some embodiments, processor 4703 may be configured to
receive information about the user's hearing capabilities and cause
the conditioning of at least one audio signal is based on the
user's hearing capabilities.
[0620] In some embodiments processor 4703 may be configured to
change a rate of speech associated with one or more audio signals.
Processor 4703 may be configured to vary the rate of speech of
sound emanating object 5110 to make the detected speech more
perceptible to user 100. Selective conditioning may also include
adding one or more spaces or pauses within the audio signal. For
example, the sound emanating object may include an individual
saying a sentence and causing the conditioning of at least one
audio signal includes adding at least one space between words in
the sentence to make the sentence more intelligible. Accordingly,
rather than hearing the spoken sentence at 1.times. speed, user 100
may hear the sentence at an increased speed (e.g., 1.1.times.,
1.5.times., 2.0.times., 2.5.times., etc.) and the space between
each word may be increased accordingly. Similarly, the spacing
between sentences may be increased, giving user 100 more time to
interpret or digest each sentence.
[0621] In some embodiments, hearing aid system 4700 may selectively
condition the audio signals based on information about the
identified sound emanating object retrieved from database 5020. For
example, processor 4703 may receive an audio signal associated with
sound 5120 from sound emanating object 5110. Based on the
identification of sound emanating object 5110, processor 4703 may
retrieve information about the sound emanating object from database
5020. For example, identifying the sound emanating object may
include determining a type of the sound emanating object, and
processor 4700 may further be programmed to cause selective
conditioning of the audio signal based on the determined type of
the at least one sound emanating object. In another embodiment, the
retrieved information may be associated with a pre-existing
relationship of user 100 with the sound emanating object, and the
at least one processor may be further programmed to cause the
selective conditioning of the at least one audio signal based on
the pre-existing relationship. In some embodiments, the selective
conditioning may also be performed based on a contextual situation
associated with user 100. The contextual situation may be
determined by analysis of one or more images captured from a camera
device, such as wearable camera 4701. The conditioning of a sound
emanating object determined through database 5020 may be different
based on the context. As an illustrative example, if the sound
emanating object is a crying baby, the selective conditioning may
include amplifying the volume of the audio signal associated with
the baby if user 100 is at home. Conversely, if hearing aid system
4700 determines user 100 is on an airplane, the selective
conditioning may include muting the audio signal associated with
the crying baby.
[0622] Where more than one sound emanating object is detected,
hearing aid system 4700 may selectively condition sounds associated
with the sound emanating objects relative to each other. In the
example scenario shown in FIG. 51A, sound emanating objects 5110
and 5111 may comprise two individuals. Processor 4703 may be
programmed to cause a first selective conditioning of audio signals
associated with a first individual (e.g., sound emanating object
5110) based on retrieved information associated with the first
individual, and cause a second selective conditioning different
from the first selective conditioning of audio signals associated
with second individual (e.g., sound emanating object 5111) based on
retrieved information associated with the second individual. For
example, the first individual may be difficult to understand and
processor 4703 may increase the volume or vary the pitch of the
audio signal associated with the first individual. Processor 4703
may determine that the audio signal associated with the second
individual is of lesser importance (e.g., based on a relationship,
importance level, etc.) and may decrease the volume associated with
the second individual. As another example, processor 4703 may
analyze a plurality of images to identify an individual (e.g.,
sound emanating object 5110) that is speaking and a sound emanating
object that generates background noises (e.g., sound emanating
object 5112). Processor 4703 may be configured to separate sounds
generated by the individual from the background noises.
Accordingly, causing selective conditioning of audio signals may
include attenuating audio signals associated with the sound
emanating object relative to the audio signals associated with the
individual. For example, if the sound emanating object is a
television, such as sound emanating object 5012, selective
conditioning of audio signals may include reducing the volume of
the television or muting it completely.
[0623] FIG. 51B is a schematic illustration showing another example
environment 5101 for selectively conditioning audio signals
consistent with the present disclosure. In this scenario user 100
may be wearing apparatus 110 and may be in the presence of sound
emanating objects 5110 and 5111, which may be individuals, as
described above. Environment 5101 may include a third sound
emanating object 5113, which may also be an individual. Processor
4703 may be configured to selectively condition audio signals
associated with sound emanating objects 5110, 5111, and 5113 based
on the interactions between sound emanating objects 5110, 5111, and
5113 and/or user 100. In the scenario shown in FIG. 51B, processor
4703 may identify a first individual (e.g., sound emanating object
5110) talking to user 100 and a second individual (e.g., sound
emanating object 5111) talking to a third individual (e.g., sound
emanating object 5113). Accordingly, processor 4703 may amplify
audio signals associated with the first individual and attenuate
audio signals associated with the second individual. In another
scenario, processor 4703 may identify a group of individuals
listening to a specific individual and may be programmed to amplify
audio signals from the specific individual.
[0624] As discussed above, database 5020 may include one or more
voiceprints associated with a particular sound emanating object.
Processor 4703 may include instructions to receive a reference
voiceprint associated with a sound emanating object that has been
identified based on the visual characteristics. Accordingly,
processor 4703 may be configured to use the plurality of images and
the reference voiceprint to identify the at least one sound
emanating object and to cause the conditioning of at least one
audio signal based on predefined settings associated with an
identity of the at least one sound emanating object. The predefined
settings may correspond to information stored in database 5020,
including rules for selectively conditioning audio, a level of
importance, a relationship with user 100, or various other
parameters that may or may not be shown in FIG. 50B. For example,
based on the voiceprint data, processor 4703 may determine that
certain sounds (e.g., a siren, a baby crying, etc.) should be heard
but may reduce the volume of background noises (e.g., an air
conditioning unit, traffic, noise office mates, etc.). In some
embodiments, processor 4703 may further use the voiceprints to
separate audio signals associated with various sound emanating
objects. For example, each sound emanating object is associated
with a unique voiceprint and processor 4703 may use voiceprints of
the sound emanating objects to separate sounds generated by a first
sound emanating object and sounds generated by a second sound
emanating object. Causing the conditioning of at least one audio
signal may include attenuating audio signals associated with the
second sound emanating object relative to the audio signals
associated with the first sound emanating object.
[0625] FIG. 52 is a flowchart showing an exemplary process 5200 for
modifying sounds emanating from objects in an environment of a user
consistent with the disclosed embodiments. Process 5200 may be
performed by a hearing aid system (e.g., hearing aid system 4700),
which may include at least one processor (e.g., processor 4703)
programmed to perform the steps described below. Processor 4703 may
correspond to one or more other processors described in detail
above, including processors 210, 210a and/or 210b. Accordingly,
process 5200 may be performed by a processor associated with a
wearable camera device, such as apparatus 110. Some or all of
process 5200 may be performed by processors associated with other
components, such as computing device 120, server 250, and/or other
devices. As described above, hearing aid system 4700 may access a
database (e.g., database 5020), which may contain information for
selectively conditioning audio for one or more sound emanating
objects. The database may be internal to the hearing aid system
(e.g., stored within memory 4704) or may be external (e.g.,
accessed via a network connection, a short-range wireless
connection, etc.). The hearing aid system may further comprise at
least one wearable camera (e.g., wearable camera 4701) and at least
one wearable microphone (e.g., microphone 4702). In some
embodiments, the wearable camera, the wearable microphone, and the
at least one processor may be included in a common housing (e.g.,
in apparatus 110). In other embodiments, the wearable camera, the
wearable microphone, and the at least one processor may be
distributed among multiple housings. For example, the wearable
camera and the wearable microphone are included in a first housing
and the at least one processor is included in a second housing
separate from the first housing.
[0626] In step S210, process 5200 may include receiving a plurality
of images captured by the wearable camera. For example, step S210
may include receiving a plurality of images captured from the
environment of the user (e.g., user 100) by the wearable camera.
Accordingly, the plurality of images may depict objects in an
environment of a user. The plurality of images may include a
representation of a sound emanating object, such as sound emanating
object 5110. In step S220, process 5200 may include receiving audio
signals acquired by the wearable microphone. The audio signals may
be representative of sounds emanating from the objects depicted in
the plurality of images received in step S210. For example,
processor 4703 may receive audio signals from microphone 4702 which
may represent sound 5120 emanating from sound emanating object
5110.
[0627] In step S230, process 5200 may include analyzing the
plurality of images to identify at least one sound emanating object
in the environment of the user. For example, processor 4703 may use
one or more image recognition techniques to extract features from
the image that are associated with sound emanating object 5110. In
some instances, the at least one sound emanating object may include
an individual and, accordingly, step S230 may include performing a
facial analysis or face recognition of an image of the individual.
In some embodiments, identifying the at least one sound emanating
object may include determining a type of the at least one sound
emanating object. For example, processor 4703 may determine whether
sound emanating object 5510 is a mechanical machine or device, a
speaker, an individual, an animal, an inanimate object, a
weather-related object, or the like.
[0628] In step S240, process 5200 may include retrieving, from a
database, information about the at least one sound emanating
object. For example, processor 4703 may access database 5020
storing information about one or more sound emanating objects. The
stored information may refer to a class of sound emanating objects
(e.g., televisions), or may refer to a specific sound emanating
object (e.g., a specific person, the user's phone, etc.). As
described above in reference to FIG. 50B, database 5020 may store
information including visual characteristics of the object, a name
of the object, a type of object, a relationship of the object to
the user, a level of importance of the object, voiceprint data
associated with the object, a rule of audio conditioning for the
object, or other information.
[0629] In step S250, process 5200 may include causing, based on the
retrieved information, selective conditioning of at least one audio
signal received by the wearable microphone from a region associated
with the at least one sound emanating object. The region may be
determined using the various methods described above (e.g., as
shown in FIG. 20A). For example, the region may be determined based
on a determined direction of the sound emanating object based on
analysis of one or more of the plurality of images or audio
signals. The range may be associated with an angular width about
the direction of the sound emanating object (e.g., 10 degrees, 20
degrees, 45 degrees, etc.).
[0630] Various forms of conditioning may be performed on the audio
signal, as discussed above. In some embodiments, conditioning may
include changing the tone or playback speed of an audio signal. For
example, conditioning may include changing a rate of speech
associated with the audio signal. As discussed above, the at least
one sound emanating object may include an individual saying a
sentence and causing the conditioning of at least one audio signal
may include adding at least one space between words in the sentence
to make the sentence more intelligible. In some embodiments, the
conditioning may include amplification of the audio signal relative
to other audio signals received from outside of the region
associated with the recognized individual. Amplification may be
performed by various means, such as operation of a directional
microphone configured to focus on audio sounds emanating from the
region, varying one or more parameters associated with the wearable
microphone to cause the microphone to focus on audio sounds
emanating from the region, modifying one or more properties of the
audio signal, or the like. The amplification may include
attenuating or suppressing one or more audio signals received by
the microphone from directions outside the region. As discussed
above, selective conditioning may depend on the preferences or
hearing capabilities of the user. For example, the retrieved
information (e.g., information received in step S240) may include
information indicative of the user's hearing capabilities and
causing the conditioning of at least one audio signal may be based
on the user's hearing capabilities.
[0631] In some embodiments, identifying the at least one sound
emanating object (e.g., in step S230) may include determining a
type of the at least one sound emanating object, and the at least
one processor may be further programmed to cause the selective
conditioning of the at least one audio signal based on the
determined type of the at least one sound emanating object. For
example, the voice of an individual may be amplified whereas the
sound from a television may be reduced or muted. In other
embodiments, the retrieved information may be associated with a
pre-existing relationship of the user with the at least one sound
emanating object, and the at least one processor may further be
programmed to cause the selective conditioning of the at least one
audio signal based on the pre-existing relationship. For example,
processor 4703 may recognize that a sound emanating object is a
phone belonging to user 100 and may amplify audio signals
associated with the phone belonging to user 100, but may not
amplify (or may mute or attenuate) audio signals associated with
other phones. Where the at least one sound emanating object
includes a plurality of objects, processor 4703 may apply a
hierarchy of amplification for audio signals associated with the
objects. In such embodiments, the hierarchy of amplification may be
based on the pre-existing relationships.
[0632] Consistent with the present disclosure, processor 4703 may
selectively condition audio associated with one sound emanating
object relative to other sound emanating objects. For example, the
at least one sound emanating object may include a plurality of
sound emanating objects, and process 5200 may further comprise
using the plurality of images to identify different types of sound
emanating objects and applying different conditioning for audio
signals received by from different regions associated with
different types of sound emanating objects. Similarly, process 5200
may further comprise analyzing the plurality of images to identify
an individual that speaks and a sound emanating object that
generates background noises, and separating sounds generated by the
individual from background noises. Causing the conditioning of at
least one audio signal may include attenuating audio signals
associated with the sound emanating object that generates
background noises relative to the audio signals associated with the
individual. For example, the sound emanating object may be a
television or a similar device and attenuating audio signals may
include muting or reducing the volume of audio signals associated
with the television.
[0633] In some embodiments, processor 4703 may be configured to
selectively condition audio signals associated with a plurality of
individuals in the environment of user 100. As discussed above,
processor 4703 may be configured to apply different conditioning
for different individuals based on the information in database
5020. For example, the at least one sound emanating object may
include a plurality of individuals, and the at least one processor
may further be programmed to cause a first selective conditioning
of audio signals associated with a first individual based on
retrieved information associated with the first individual and
cause a second selective conditioning different from the first
selective conditioning of audio signals associated with a second
individual based on retrieved information associated with the
second individual.
[0634] Processor 4703 may further selectively condition audio
signals based on actions of the individuals. For example, the at
least one sound emanating object may include a plurality of
individuals and the at least one processor may further be
programmed to identify in the plurality of images a first
individual talking to the user and a second individual talking to a
third individual. The at least one processor may amplify audio
signals from the first individual and attenuate audio signals from
the second individual. Accordingly, audio associated with the first
individual, who is talking to the user, may be more easily
perceptible than audio associated with the second individual. As
another example, the at least one sound emanating object may
include a plurality of individuals and the at least one processor
may further be programmed to identify in the plurality of images a
group of individuals listening to a specific individual and to
amplify audio signals from the specific individual.
[0635] In some embodiments, processor 4703 may selectively
condition audio signals based on a detected speaker. Processor 4703
may automatically switch between speakers based on another
individual beginning to speak. For example, the plurality of sound
emanating objects may include a plurality of individuals, and
process 5200 may comprise using the plurality of images to
determine that a first individual is talking; amplifying audio
signals received from a region associated with the first
individual; using the plurality of images to determine that a
second individual is about to talk and amplify audio signals
received from a region associated with the second individual
instead of audio signals received from the region associated with
the first individual. For example, processor 4703 may be configured
to detect facial features of the second individual and may
automatically switch to selectively condition audio signals
associated with the second individual when they open their mouth,
etc.
[0636] In some embodiments, processor 4703 may also determine
and/or retrieve voiceprint data associated with sound emanating
objects for the purposes of selectively conditioning audio
associated with the sound emanating objects. For example, the
retrieved information (e.g., information retrieved from database
5020 in step S240) may include a reference voiceprint associated
with the at least one sound emanating object. In some embodiments,
process 5200 may further comprise using the plurality of images and
the reference voiceprint to identify the at least one sound
emanating object, separate the audio signal associated with the
reference voiceprint, and cause the conditioning of the audio
signal based on predefined settings associated with an identity of
the at least one sound emanating object. For example, processor
4703 may amplify an audio signal associated with a close family
member of user 100 but may attenuate or mute audio associated with
other individuals, such as a noisy office mate. Database 5020 may
store more than one voiceprint for each sound emanating object. For
example, the at least one sound emanating object may include a
plurality of individuals and the retrieved information may include
a set of reference voiceprints for each individual. A first
voiceprint of a specific individual may be associated with a
scenario where the specific individual is standing next to the
user, and a second voiceprint of the specific individual may be
associated with a scenario where the specific individual is talking
through a communication device. Accordingly, processor 4703 may
selectively condition the voice of an individual regardless of
whether they are standing next to the user or if they are talking
on a speaker phone.
[0637] The voiceprint data may also be used to improve selective
conditioning of audio signals. For example, process 5200 may
further comprise analyzing the plurality of images to identify a
plurality of sound emanating objects in the environment of the
user, wherein each sound emanating object is associated with a
unique voiceprint. Process 5200 may include using voiceprints of
the plurality of sound emanating objects to separate sounds
generated by a first sound emanating object and sounds generated by
a second sound emanating object, and causing the conditioning of at
least one audio signal may include attenuating audio signals
associated with the second sound emanating object relative to the
audio signals associated with the first sound emanating object.
[0638] As described above, selective conditioning may further be
based on contextual information associated with user 100 or the at
least one sound emanating object. For example, process 5200 may
further comprise identifying, based on analysis of the plurality of
images, a contextual situation associated with one or more of the
plurality of images; retrieving, from the database, information
associated with the contextual situation; and causing a first
selective conditioning of audio signals from a specific object in
response to a first detected contextual situation and cause a
second selective conditioning, different from the first selective
conditioning of audio signals from the specific object, in response
to a second detected contextual situation.
[0639] In step S260, process 5200 may comprise causing transmission
of the at least one conditioned audio signal to a hearing interface
device configured to provide sounds to an ear of the user. The
conditioned audio signal, for example, may be transmitted to
hearing interface device 1710, which may provide sound
corresponding to the audio signal to user 100. Processor 4703 may
be configured to transmit the conditioned audio signal in real time
(or after a very short delay). For example, the at least one
processor may be programmed to cause transmission of the at least
one conditioned audio signal to the hearing interface device in
less than 100 mSec (e.g, 10 mSec, 20 mSec, 30 mSec, 50 mSec, etc.)
after the at least one audio signal was acquired by the wearable
microphone. The processor performing process 1900 may further be
configured to cause transmission to the hearing interface device of
one or more audio signals representative of other sound emanating
objects, which may also be conditioned. Accordingly, the hearing
interface device may comprise a receiver configured to receive at
least one audio signal. As discussed above, the at least one audio
signal may have been acquired by a wearable microphone and may have
been selectively conditioned by at least one processor configured
to receive a plurality of images captured by a wearable camera,
identify at least one sound emanating object in the plurality of
images, and cause the conditioning based on retrieved information
about the at least one sound emanating object. The hearing
interface device may further comprise an electroacoustic transducer
configured to provide sounds from the at least one audio signal to
an ear of the user. The hearing aid device may also comprise other
elements, such as those described above with respect to hearing
interface device 1710. In some embodiments, the hearing interface
device may include a bone conduction microphone, configured to
provide an audio signal to user through vibrations of a bone of the
user's head. Such devices may be placed in contact with the
exterior of the user's skin or may be implanted surgically and
attached to the bone of the user.
[0640] The foregoing description has been presented for purposes of
illustration. It is not exhaustive and is not limited to the
precise forms or embodiments disclosed. Modifications and
adaptations will be apparent to those skilled in the art from
consideration of the specification and practice of the disclosed
embodiments. Additionally, although aspects of the disclosed
embodiments are described as being stored in memory, one skilled in
the art will appreciate that these aspects can also be stored on
other types of computer readable media, such as secondary storage
devices, for example, hard disks or CD ROM, or other forms of RAM
or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, or other optical
drive media.
[0641] Computer programs based on the written description and
disclosed methods are within the skill of an experienced developer.
The various programs or program modules can be created using any of
the techniques known to one skilled in the art or can be designed
in connection with existing software. For example, program sections
or program modules can be designed in or by means of .Net
Framework, .Net Compact Framework (and related languages, such as
Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX
combinations, XML, or HTML with included Java applets.
[0642] Moreover, while illustrative embodiments have been described
herein, the scope of any and all embodiments having equivalent
elements, modifications, omissions, combinations (e.g., of aspects
across various embodiments), adaptations and/or alterations as
would be appreciated by those skilled in the art based on the
present disclosure. The limitations in the claims are to be
interpreted broadly based on the language employed in the claims
and not limited to examples described in the present specification
or during the prosecution of the application. The examples are to
be construed as non-exclusive. Furthermore, the steps of the
disclosed methods may be modified in any manner, including by
reordering steps and/or inserting or deleting steps. It is
intended, therefore, that the specification and examples be
considered as illustrative only, with a true scope and spirit being
indicated by the following claims and their full scope of
equivalents.
* * * * *