U.S. patent application number 17/145431 was filed with the patent office on 2022-07-14 for speech filtering for masks.
This patent application is currently assigned to Ford Global Technologies, LLC. The applicant listed for this patent is Ford Global Technologies, LLC. Invention is credited to SCOTT ANDREW AMMAN, BRIAN BENNIE, PIETRO BUTTOLO, CYNTHIA M. NEUBECKER, JOHN ROBERT VAN WIEMEERSCH, JOSHUA WHEELER.
Application Number | 20220223145 17/145431 |
Document ID | / |
Family ID | |
Filed Date | 2022-07-14 |
United States Patent
Application |
20220223145 |
Kind Code |
A1 |
AMMAN; SCOTT ANDREW ; et
al. |
July 14, 2022 |
SPEECH FILTERING FOR MASKS
Abstract
A computer includes a processor and a memory storing
instructions executable by the processor to receive sensor data of
an occupant of a vehicle, identify a type of mask worn by the
occupant based on the sensor data, select a sound filter according
to the type of mask from a plurality of sound filters stored in the
memory, receive sound data, apply the selected sound filter to the
sound data, and perform an operation using the filtered sound
data.
Inventors: |
AMMAN; SCOTT ANDREW;
(Milford, MI) ; NEUBECKER; CYNTHIA M.; (Westland,
MI) ; WHEELER; JOSHUA; (Trenton, MI) ;
BUTTOLO; PIETRO; (Dearborn Heights, MI) ; BENNIE;
BRIAN; (Sterling Heights, MI) ; VAN WIEMEERSCH; JOHN
ROBERT; (Novi, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ford Global Technologies, LLC |
Dearborn |
MI |
US |
|
|
Assignee: |
Ford Global Technologies,
LLC
Dearborn
MI
|
Appl. No.: |
17/145431 |
Filed: |
January 11, 2021 |
International
Class: |
G10L 15/22 20060101
G10L015/22; G06K 9/00 20060101 G06K009/00; G10L 15/07 20060101
G10L015/07 |
Claims
1. A computer comprising a processor and a memory storing
instructions executable by the processor to: receive sensor data of
an occupant of a vehicle; identify a type of mask worn by the
occupant based on the sensor data; select a sound filter according
to the type of mask from a plurality of sound filters stored in the
memory; receive sound data; apply the selected sound filter to the
sound data; and perform an operation using the filtered sound
data.
2. The computer of claim 1, wherein the sensor data is image data
showing the occupant.
3. The computer of claim 1, wherein the operation is identifying a
voice command to activate a feature.
4. The computer of claim 1, wherein the operation is transmitting
the filtered sound data in a telephone call.
5. The computer of claim 1, wherein the operation is outputting the
filtered sound data by a speaker of the vehicle.
6. The computer of claim 1, wherein the instructions include
instructions to perform the operation using the sound data
unfiltered upon determining that the occupant is not wearing a
mask.
7. The computer of claim 1, wherein the instructions include
instructions to select a generic sound filter from the plurality of
sound filters upon identifying the type of mask as an unknown
type.
8. The computer of claim 7, wherein the instructions include
instructions to transmit an update to a remote server upon
identifying the type of mask as the unknown type.
9. The computer of claim 8, wherein the update includes image data
of the mask.
10. The computer of claim 1, wherein the instructions include
instructions to identify the type of mask worn by the occupant
based on an input by the occupant.
11. The computer of claim 10, wherein the instructions include
instructions to override the identification based on the sensor
data with the identification based on the input upon receiving the
input.
12. The computer of claim 10, wherein the instructions include
instructions to prompt the occupant to provide the input upon
determining that the occupant is wearing a mask.
13. The computer of claim 10, wherein the instructions include
instructions to prompt the occupant to provide the input upon
determining that one of the occupant is wearing a mask with a type
identified with a confidence score below a confidence threshold or
the type of the mask is an unknown type.
14. The computer of claim 10, wherein the instructions include
instructions to transmit an update to a remote server in response
to the input indicating that the type of the mask is not among the
types of masks stored in the memory.
15. The computer of claim 1, wherein the instructions include
instructions to choose the occupant for which to identify the type
of mask from a plurality of occupants based on volumes of sound
data from respective microphones.
16. The computer of claim 1, wherein the instructions include
instructions to choose the occupant for which to identify the type
of mask from a plurality of occupants based on the occupant being
in a predesignated region of the image data.
17. The computer of claim 1, wherein each sound filter adjusts a
volume of the sound data by an amount that varies depending on
frequency.
18. The computer of claim 17, wherein each sound filter increases
the volume of the sound data at at least one frequency.
19. The computer of claim 1, wherein the instructions include
instructions to receive an update from a remote server changing the
plurality of sound filters stored in the memory.
20. A method comprising: receiving sensor data of an occupant of a
vehicle; identifying a type of mask worn by the occupant based on
the sensor data; selecting a sound filter according to the type of
mask from a plurality of sound filters stored in memory; receiving
sound data; applying the selected sound filter to the sound data;
and performing an operation using the filtered sound data.
Description
BACKGROUND
[0001] Many modern vehicles include voice-recognition systems. Such
a system includes a microphone. The system converts spoken words
detected by the microphone into text or another form to which a
command can be matched. Recognized commands can include adjusting
climate controls, selecting media to play, etc.
BRIEF DESCRIPTION OF THE DRAWINGS
[0002] FIG. 1 is a top view of an example vehicle with a passenger
cabin exposed for illustration.
[0003] FIG. 2 is a block diagram of a system of the vehicle.
[0004] FIG. 3 is a process flow diagram of an example process for
filtering speech of an occupant of the vehicle wearing a mask.
[0005] FIG. 4 is a plot of sound pressure versus frequency for
speech while wearing a mask for a plurality of masks.
DETAILED DESCRIPTION
[0006] A computer includes a processor and a memory storing
instructions executable by the processor to receive sensor data of
an occupant of a vehicle, identify a type of mask worn by the
occupant based on the sensor data, select a sound filter according
to the type of mask from a plurality of sound filters stored in the
memory, receive sound data, apply the selected sound filter to the
sound data, and perform an operation using the filtered sound
data.
[0007] The sensor data may be image data showing the occupant.
[0008] The operation may be identifying a voice command to activate
a feature.
[0009] The operation may be transmitting the filtered sound data in
a telephone call.
[0010] The operation may be outputting the filtered sound data by a
speaker of the vehicle.
[0011] The instructions may include instructions to perform the
operation using the sound data unfiltered upon determining that the
occupant is not wearing a mask.
[0012] The instructions may include selecting a generic sound
filter from the plurality of sound filters upon identifying the
type of mask as an unknown type. The instructions may include
instructions to transmit an update to a remote server upon
identifying the type of mask as the unknown type. The update may
include image data of the mask.
[0013] The instructions may include instructions to identify the
type of mask worn by the occupant based on an input by the
occupant. The instructions may include instructions to override the
identification based on the sensor data with the identification
based on the input upon receiving the input.
[0014] The instructions may include instructions to prompt the
occupant to provide the input upon determining that the occupant is
wearing a mask.
[0015] The instructions may include instructions to prompt the
occupant to provide the input upon determining that one of the
occupant is wearing a mask with a type identified with a confidence
score below a confidence threshold or the type of the mask is an
unknown type.
[0016] The instructions may include instructions to transmit an
update to a remote server in response to the input indicating that
the type of the mask is not among the types of masks stored in the
memory.
[0017] The instructions may include instructions to choose the
occupant for which to identify the type of mask from a plurality of
occupants based on volumes of sound data from respective
microphones.
[0018] The instructions may include instructions to choose the
occupant for which to identify the type of mask from a plurality of
occupants based on the occupant being in a predesignated region of
the image data.
[0019] Each sound filter may adjust a volume of the sound data by
an amount that varies depending on frequency. Each sound filter
increases the volume of the sound data at at least one
frequency.
[0020] The instructions may include instructions to receive an
update from a remote server changing the plurality of sound filters
stored in the memory.
[0021] A method includes receiving sensor data of an occupant of a
vehicle, identifying a type of mask worn by the occupant based on
the sensor data, selecting a sound filter according to the type of
mask from a plurality of sound filters stored in memory, receiving
sound data, applying the selected sound filter to the sound data,
and performing an operation using the filtered sound data.
[0022] With reference to the Figures, a computer 100 includes a
processor and a memory storing instructions executable by the
processor to receive sensor data of an occupant of a vehicle 102,
identify a type of mask worn by the occupant based on the sensor
data, select a sound filter according to the type of mask from a
plurality of sound filters stored in the memory, receive sound
data, apply the selected sound filter to the sound data, and
perform an operation using the filtered sound data.
[0023] The computer 100 can be used to boost the clarity of speech
from an occupant wearing a mask by selecting the type of mask and
thereby applying the filter most appropriate to equalize the
speech. The choice of filter permits the frequencies muffled by
that particular mask to be amplified. The filtered sound data can
thus reliably be used to perform operations such as a voice command
to activate a feature of the vehicle 102, a transmission in a
telephone call, or broadcasting as a telecom to a speaker 114
elsewhere in the vehicle 102. The voice command can be reliably
recognized, the telephone call can be reliably understood by the
person at the other end from the occupant, and the telecom message
can be reliably understood by the other occupant of the vehicle
102.
[0024] With reference to FIG. 1, the vehicle 102 may be any
suitable type of automobile, e.g., a passenger or commercial
automobile such as a sedan, a coupe, a truck, a sport utility, a
crossover, a van, a minivan, a taxi, a bus, etc. The vehicle 102,
for example, may be autonomous. In other words, the vehicle 102 may
be autonomously operated such that the vehicle 102 may be driven
without constant attention from a driver, i.e., the vehicle 102 may
be self-driving without human input.
[0025] The vehicle 102 includes a passenger cabin 104 to house
occupants of the vehicle 102. The passenger cabin 104 includes one
or more front seats 106 disposed at a front of the passenger cabin
104 and one or more back seats 106 disposed behind the front seats
106. The passenger cabin 104 may also include third-row seats 106
(not shown) at a rear of the passenger cabin 104.
[0026] The vehicle 102 includes at least one camera 108. The camera
108 can detect electromagnetic radiation in some range of
wavelengths. For example, the camera 108 may detect visible light,
infrared radiation, ultraviolet light, or some range of wavelengths
including visible, infrared, and/or ultraviolet light. For example,
the camera 108 can be a thermal imaging camera.
[0027] The camera 108 is positioned so that a field of view of the
camera 108 encompasses at least one of the seats 106, e.g., the
driver seat 106, or the front and back seats 106. For example, the
camera 108 can be positioned on an instrument panel 118 or
rear-view mirror and oriented rearward relative to the passenger
cabin 104.
[0028] The vehicle 102 includes at least one microphone 110, e.g.,
a first microphone 110a and a second microphone 110b. The
microphones 110 are transducers that convert sound into an
electrical signal. The microphones 110 can be any suitable type of
microphones for detecting speech by occupants of the vehicle 102,
e.g., dynamic, condenser, contact, etc.
[0029] The microphones 110 can be arranged at respective locations
or positions in the passenger cabin 104 to collectively detect
speech from occupants in different seats 106. For example, the
first microphone 110a can be positioned in the instrument panel
118, and the second microphone 110b can be positioned between the
front seats 106 and oriented to pick up sound from the back seats
106.
[0030] A user interface 112 presents information to and receives
information from an occupant of the vehicle 102. The user interface
112 may be located, e.g., on the instrument panel 118 in the
passenger cabin 104, or wherever it may be readily seen by the
occupant. The user interface 112 may include dials, digital
readouts, screens, speakers 114, and so on for providing
information to the occupant, e.g., human-machine interface (HMI)
elements such as are known. The user interface 112 may include
buttons, knobs, keypads, the microphones 110, and so on for
receiving information from the occupant.
[0031] The speakers 114 are electroacoustic transducers that
convert an electrical signal into sound. The speakers 114 can be
any suitable type for producing sound audible to the occupants,
e.g., dynamic. The speakers 114 can be arranged at respective
locations or positions in the passenger cabin 104 to collectively
produce sound for occupants in respective seats 106.
[0032] With reference to FIG. 2, the computer 100 is a
microprocessor-based computing device, e.g., a generic computing
device including a processor and a memory, an electronic controller
or the like, a field-programmable gate array (FPGA), an
application-specific integrated circuit (ASIC), etc. The computer
100 can thus include a processor, a memory, etc. The memory of the
computer 100 can include media for storing instructions executable
by the processor as well as for electronically storing data and/or
databases, and/or the computer 100 can include structures such as
the foregoing by which programming is provided. The computer 100
can be multiple computers coupled together.
[0033] The computer 100 may transmit and receive data through a
communications network 116 such as a controller area network (CAN)
bus, Ethernet, WiFi.RTM., Local Interconnect Network (LIN), onboard
diagnostics connector (OBD-II), and/or by any other wired or
wireless communications network. The computer 100 may be
communicatively coupled to the camera 108, the microphones 110, the
user interface 112, the speakers 114, a transceiver 118, and other
components via the communications network 116.
[0034] The transceiver 118 may be connected to the communications
network. The transceiver 118 may be adapted to transmit signals
wirelessly through any suitable wireless communication protocol,
such as cellular, Bluetooth.RTM., Bluetooth.RTM. Low Energy (BLE),
ultra-wideband (UWB), WiFi, IEEE 802.11a/b/g, other RF (radio
frequency) communications, etc. The transceiver 118 may be adapted
to communicate with a remote server 120, that is, a server distinct
and spaced from the vehicle 102. The remote server 120 may be
located outside the vehicle 102. For example, the remote server 120
may be associated with another vehicle (e.g., V2V communications),
an infrastructure component (e.g., V2I communications via Dedicated
Short-Range Communications (DSRC) or the like), an emergency
responder, a mobile device associated with the owner of the vehicle
102, etc. The transceiver 118 may be one device or may include a
separate transmitter and receiver.
[0035] With reference to FIG. 4, the computer 100 stores a
plurality of sound filters in memory. Each sound filter specifies
how much to adjust a sound pressure, i.e., volume, of sound data
according to a frequency, e.g., each sound filter provides sound
pressure as a mathematical function of frequency, SP=F(f), in which
SP is sound pressure, F is the sound filter, and f is frequency.
The sound filter F.sub.i can be a difference of a baseline sound
pressure SP.sub.base and a sound pressure for a type of mask
SP.sub.i, i.e., F.sub.i(f)=SP.sub.base(f)-SP.sub.i(f), in which i
is an index of the type of mask. Masks often have a small effect on
volume when the frequency is 500 Hz or less and muffle sounds more
considerably at 1000 Hz and higher to an extent that depends on the
type of mask. One of the sound filters stored in memory is
associated with the unknown type of mask, and that sound filter can
be a generic sound filter, e.g., an average of the other sound
filters stored in memory.
[0036] The sound filters stored in memory can be updated from the
remote server 120, e.g., an over-the-air (OTA) update via the
transceiver 118. An update can add new sound filters for a new type
of mask for which a sound filter is not already stored by the
computer 100. Alternatively or additionally, the update can change
one or more of the sound filters already stored by the computer
100. Thus, the sound filters stored by the computer 100 can be
updated as new types of masks are introduced, materials of existing
masks change, etc. The update can occur periodically or on
demand.
[0037] FIG. 3 is a process flow diagram illustrating an exemplary
process 300 for filtering speech of an occupant of the vehicle 102
wearing a mask. The memory of the computer 100 stores executable
instructions for performing the steps of the process 300 and/or
programming can be implemented in structures such as mentioned
above. As a general overview of the process 300, the computer 100
receives data from the camera 108 and the microphones 110, detects
a mask worn by an occupant based on the data, and identifies the
type of the mask. If the occupant is wearing a mask of a type
identified with a confidence score above a confidence threshold,
the computer 100 selects a sound filter corresponding to the type
of mask. If the occupant is wearing a mask of a type identified
with a confidence score above a confidence threshold, the computer
100 prompts input from the occupant about the type of mask and
selects a sound filter corresponding to the type of mask either
inputted by the occupant or identified by the computer 100. The
computer 100 applies the selected sound filter to sound data, and
performs an operation using the filtered sound data. If there are
no masks, the computer 100 performs the operation based on the
unfiltered sound data.
[0038] The process 300 begins in a block 305, in which the computer
100 receives sensor data of at least one occupant of the vehicle
102, e.g., image data from the camera 108 showing the occupants
and/or sound data from the microphones 110 of speech by the
occupants.
[0039] Next, in a block 310, the computer 100 detects a mask worn
by one of the occupants. If a plurality of occupants are in the
passenger cabin 104, the computer 100 chooses one of the occupants.
For example, the computer 100 can choose the occupant based on the
occupant being in a predesignated region of the image data, e.g.,
corresponding to an occupant sitting in a particular seat 106 such
as an operator seat 106, and then detect the mask worn by that
occupant. This can permit the computer 100 to detect a mask worn by
the operator of the vehicle 102. For another example, the computer
100 can choose the occupant based on volumes of sound data from the
respective microphones 110, e.g., based on the microphone 110 with
the highest volume, and then detect the mask worn by the occupant
closest to that microphone 110. This can permit the computer 100 to
detect a mask worn by an occupant most likely to be speaking for
performing the operation, e.g., an occupant sitting in the back
seat 106 when the volume from the microphone 110b is greater than
from the microphone 110a. The computer 100 can identify the mask or
unmasked face using conventional image-recognition techniques,
e.g., a convolutional neural network programmed to accept images as
input and output an identified mask presence or absence. The image
data from the camera 108 can be used as the input. The
convolutional neural network can use images of occupants of
vehicles wearing and not wearing masks produced by cameras situated
in the same location as the camera 108. A convolutional neural
network includes a series of layers, with each layer using the
previous layer as input. Each layer contains a plurality of neurons
that receive as input data generated by a subset of the neurons of
the previous layers and generate output that is sent to neurons in
the next layer. Types of layers include convolutional layers, which
compute a dot product of a weight and a small region of input data;
pool layers, which perform a down-sampling operation along spatial
dimensions; and fully connected layers, which generate outputs
based on the output of all neurons of the previous layer. The final
layer of the convolutional neural network generates a confidence
score for mask and for unmasked face, and the final output is
whichever of mask or unmasked face has the highest confidence
score. For the purposes of this disclosure, a "confidence score" is
a measure of a probability that the identification is correct. The
identification of an occupant face as masked or unmasked can be
performed for respective occupants in the passenger cabin 104.
Alternatively or additionally, the computer 100 may detect masks
worn by multiple occupants.
[0040] Next, in a block 315, the computer 100 identifies the types
of masks worn by the occupants. The computer 100 can execute a
convolutional neural network as described above for each detected
mask using the image data, and the output is the type of mask with
the highest confidence score for each occupant. The convolutional
neural network can operate on the image data of the mask, or
alternatively on image data of a logo on the mask. The types of
masks can be specified by, e.g., manufacturer and model, e.g., 3M
1860, 3M 1870, Kimberly-Clark 49214, Scott Xcel, etc. One of the
possible types of masks is an unknown type, i.e., a mask that is
none of the masks stored in memory. Alternatively, a single
convolutional neural network can be executed for the blocks 310 and
315, and the output for each occupant is one of the types of masks,
the unknown type, or unmasked face, whichever has the highest
confidence score. Alternatively or additionally, the computer 100
may identify types of masks (or unmasked face) worn by multiple
occupants. If the identification of the type of mask is the unknown
type, the computer 100 transmits an update to the remote server 120
via the transceiver 118. The update can include the image data
showing the mask of unknown type.
[0041] Next, in a decision block 320, the computer 100 determines
whether the occupant is wearing a mask, i.e., whether the output of
the convolutional neural network(s) is mask and/or a type of mask
for the occupant, and the computer 100 determines whether the
confidence score of the type of mask is above a confidence
threshold. The confidence threshold can be chosen to indicate a
high likelihood that the type of mask is correctly identified. Upon
determining that the occupant is wearing a mask and that the
confidence score for the type of mask is below the threshold score
(or if the identified type of mask is the unknown type), the
process 300 proceeds to a block 325. Upon determining that the
occupant is wearing a mask and that the confidence score for the
type of mask is above the threshold score, the process 300 proceeds
to a block 335. Upon determining that the occupant is not wearing a
mask, the process 300 proceeds to a block 355.
[0042] In the block 325, the computer 100 prompts the occupants to
provide an input through the user interface 112 specifying a type
of mask that the occupant is wearing. For example, the user
interface 112 can present a list of types of masks for the occupant
to choose from. The list can be a default list stored in memory.
Alternatively, the list can include the types of masks with the
highest confidence scores as determined in the block 315, or the
user interface 112 can display a single type of mask with the
highest confidence score and ask the occupant to confirm that the
type of mask is correct. The list can include an option, e.g.,
"other," for indicating that the type of the mask is not among the
types of masks stored by the computer 100. Selecting this option
can be treated as though the occupant selected that the type of the
mask is the unknown type. When this option is selected, the
computer 100 can transmit an update to the remote server 120 via
the transceiver 118, if the computer 100 did not already do so in
the block 315. The update can include the image data showing the
mask of unknown type.
[0043] Next, in a decision block 330, the computer 100 determines
whether the occupant inputted a type of mask in response to the
prompt in the block 325. The occupant provides the input by
selecting the type of mask from the list, and the occupant can fail
to provide the input by selecting an option declining to provide a
type of mask, e.g., an option labeled "Choose mask automatically,"
or by failing to select a type of mask within a time threshold. The
time threshold can be chosen to provide the occupant sufficient
time to response to the prompt. If the occupant did not select a
type of mask, the process 300 proceeds to a block 335. If the
occupant selected a type of mask, the process 300 proceeds to a
block 340.
[0044] In the block 335, the computer 100 selects a sound filter
according to the type of mask identified in the block 315 from the
plurality of the sound filters stored in memory. Selecting from the
plurality of sound filters can provide a sound filter that most
accurately adjusts the sound data to the baseline level.
[0045] Alternatively, when the computer 100 has identified multiple
types of masks, the computer 100 can select multiple sound filters,
each associated with one of the identified types of masks. The
computer 100 can combine the sound filters together, e.g., by
simple averaging or by weighting. The sound filters can be weighted
based on locations of the occupants wearing the masks relative to
one of the microphones 110 generating sound data, e.g., based on
volumes of the sound data from the respective microphones 110. If
the first microphone 110a is generating sound data with greater
volume than the second microphone 110b, then the sound filters are
weighted according to relative distances of the masks of each type
from the chosen microphone 110a. For example, if a mask of a type 1
is a distance d.sub.1 from the chosen microphone 110a and a mask of
a type 2 is a distance d.sub.2 from the chosen microphone 110a,
then the weights can be w.sub.1=d.sub.1/(d.sub.1+d.sub.2) and
w.sub.2=d.sub.2/(d.sub.1+d.sub.2), and the combined sound filter
can be F.sub.combo(f)=w.sub.1*F.sub.1(f)+w.sub.2*F.sub.2(f). After
the block 335, the process 300 proceeds to a block 345.
[0046] In the block 340, the computer 100 identifies the type of
mask based on the input by the occupant and selects the sound
filter from memory associated with the identified type of mask. In
other words, the computer 100 overrides the identification based on
the image data or sound data with the identification based on the
input upon receiving the input, by executing the block 340 instead
of the block 335. After the block 340, the process 300 proceeds to
a block 345.
[0047] In the block 345, the computer 100 receives sound data from
the microphones 110. The sound data can include speech by the
occupants.
[0048] Next, in a block 350, the computer 100 applies the selected
sound filter or the combination of the selected sound filters to
the sound data. The sound filter adjusts a volume of the sound data
by an amount that varies depending on the frequency. For example,
for each frequency f of the sound data, the sound filter adjusts
the sound pressure, i.e., adjusts the volume, by the value of the
sound filter for that frequency, e.g.,
SP.sub.filt(f)=F(t)+SP.sub.unfilt(f). For example, the sound filter
can adjust the volume only slightly when the frequency is 500 Hz or
less and increase the volume more considerably at 1000 Hz and
higher to an extent that depends on the type of mask. After the
block 350, the process 300 proceeds to a block 360.
[0049] In the block 355, i.e., after not detecting any masks, the
computer 100 receives sound data from the microphones 110. The
sound data can include speech by the occupants. After the block
355, the process 300 proceeds to the block 360.
[0050] In the block 360, the computer 100 performs an operation
using the sound data, either the filtered sound data from the block
350 or the unfiltered sound data from the block 355. For example,
the operation can be identifying a voice command to activate a
feature, e.g., converting the sound data into text such as "Call
Pizza Place," "Play Podcast," "Decrease Temperature," etc. (or into
equivalent data identifying the command) Using the filtered sound
data can help the computer 100 to accurately identify the voice
command. For another example, the operation can be transmitting the
sound data in a telephone call. A mobile phone can be paired with
the user interface 112 and used to place a telephone call. Using
the filtered sound data can make it easy for the recipient of the
call to understand what the occupant is saying. For another
example, the operation can be outputting the filtered sound data by
one or more of the speakers 114. Sound data originating from the
first microphone 110 can be used and outputted by the speaker 114
at a rear of the passenger cabin 104; in other words, the first
microphone 110 and the speaker 114 form a telecom. Using the
filtered sound data can make it easier for an occupant in the back
seat 106 to understand what the occupant in the front seat 106 is
saying than directly hearing the occupant speaking while muffled by
the mask. After the block 360, the process 300 ends.
[0051] Computer executable instructions may be compiled or
interpreted from computer programs created using a variety of
programming languages and/or technologies, including, without
limitation, and either alone or in combination, Java.TM., C, C++,
Visual Basic, Java Script, Perl, HTML, etc. In general, a processor
(e.g., a microprocessor) receives instructions, e.g., from a
memory, a computer 100 readable medium, etc., and executes these
instructions, thereby performing one or more processes, including
one or more of the processes described herein. Such instructions
and other data may be stored and transmitted using a variety of
computer readable media. A file in a networked device is generally
a collection of data stored on a computer readable medium, such as
a storage medium, a random-access memory, etc. A computer readable
medium includes any medium that participates in providing data
(e.g., instructions), which may be read by a computer. Such a
medium may take many forms, including, but not limited to,
nonvolatile media, volatile media, etc. Nonvolatile media include,
for example, optical or magnetic disks and other persistent memory.
Volatile media include dynamic random-access memory (DRAM), which
typically constitutes a main memory. Common forms of computer
readable media include, for example, a floppy disk, a flexible
disk, hard disk, magnetic tape, any other magnetic medium, a CD
ROM, DVD, any other optical medium, punch cards, paper tape, any
other physical medium with patterns of holes, a RAM, a PROM, an
EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any
other medium from which a computer can read.
[0052] The disclosure has been described in an illustrative manner,
and it is to be understood that the terminology which has been used
is intended to be in the nature of words of description rather than
of limitation. Use of "in response to" and "upon determining"
indicates a causal relationship, not merely a temporal
relationship. The adjectives "first" and "second" are used
throughout this document as identifiers and are not intended to
signify importance, order, or quantity. Many modifications and
variations of the present disclosure are possible in light of the
above teachings, and the disclosure may be practiced otherwise than
as specifically described.
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