U.S. patent application number 15/189423 was filed with the patent office on 2016-10-13 for system and method for audio enhancement of a consumer electronics device.
The applicant listed for this patent is Actiwave AB. Invention is credited to Richard Kjerstadius, Par Gunnars Risberg, Landy Toth.
Application Number | 20160302017 15/189423 |
Document ID | / |
Family ID | 48781845 |
Filed Date | 2016-10-13 |
United States Patent
Application |
20160302017 |
Kind Code |
A1 |
Risberg; Par Gunnars ; et
al. |
October 13, 2016 |
SYSTEM AND METHOD FOR AUDIO ENHANCEMENT OF A CONSUMER ELECTRONICS
DEVICE
Abstract
Systems and methods for enhancing the audio experience on a
consumer electronic device are disclosed. More particularly systems
and methods for optimizing the audio performance of individual
consumer electronic devices as part of a manufacturing process
and/or retail experience are disclosed. A system for enhancing the
audio performance of a consumer electronic device including a
parametrically configurable processing block is disclosed.
Inventors: |
Risberg; Par Gunnars;
(Solna, SE) ; Kjerstadius; Richard; (Stockholm,
SE) ; Toth; Landy; (Doylestown, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Actiwave AB |
Solna |
|
SE |
|
|
Family ID: |
48781845 |
Appl. No.: |
15/189423 |
Filed: |
June 22, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14370994 |
Jul 8, 2014 |
9386386 |
|
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PCT/US13/20734 |
Jan 9, 2013 |
|
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15189423 |
|
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61584462 |
Jan 9, 2012 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/165 20130101;
H04R 29/001 20130101; H04R 29/00 20130101; H04R 5/033 20130101;
H04R 2499/11 20130101; H04R 5/04 20130101 |
International
Class: |
H04R 29/00 20060101
H04R029/00; H04R 5/033 20060101 H04R005/033; G06F 3/16 20060101
G06F003/16 |
Claims
1. A system for optimizing the audio performance of a consumer
electronics device comprising: an audio testing component for
deriving an audio test dataset from the consumer electronics
device; a master design record for outputting a reference dataset;
an audio parameter generator for deriving one or more optimal audio
parameters from the audio test dataset and the reference dataset;
and a programming unit to program the optimal audio parameters onto
the consumer electronics device.
2. The system in accordance with claim 1, wherein the audio
parameter generator comprises a probabilistic model for determining
the optimal audio parameters.
3. The system in accordance with claim 2, comprising a machine
learning algorithm for training the probabilistic model.
4. The system in accordance with claim 2, wherein the probabilistic
model is selected from a group consisting of a Kalman filter, a
Markov model, a neural network, a Bayesian network, a fuzzy
network, a self-organizing map, a dynamic Bayesian network and
combinations thereof.
5. The system in accordance with claim 1, wherein the master design
record comprises at least a portion of a history of audio test
datasets and associated optimal audio parameters.
6. The system in accordance with claim 1, comprising an acoustic
analysis unit for generating a relative dataset from the reference
dataset and the audio test dataset, the audio parameter generator
configured to accept the relative dataset for use in generating the
optimal audio parameters.
7. The system in accordance with claim 6, wherein the acoustic
analysis unit comprises a feature extraction block to derive one or
more audio features from the audio test dataset and/or the
reference dataset, the audio features included in the relative
dataset.
8. The system in accordance with claim 1, wherein the acoustic
analysis unit comprises a variance analysis block to derive an
audio variance dataset from the audio test dataset and the
reference dataset, the audio variance dataset included in the
relative dataset.
9. The system in accordance with claim 1, comprising a manual
parameter building interface comprising a display and a data input
device for interfacing with a human user.
10. The system in accordance with claim 9, wherein the manual
parameter building interface comprises a toolset to allow a human
user to generate the optimal audio parameters, bypassing the audio
parameter generator.
11. A tuning rig for optimizing the acoustic performance of a
consumer electronics device configured to accept one or more
programmable audio parameters, comprising: an acoustic test chamber
configured to accept the consumer electronics device; one or more
microphones placed within the acoustic test chamber; and a
workstation in operable communication with the consumer electronics
device and the microphones, configured to deliver one or more audio
test signals to the consumer electronics device, receive one or
more measured signals from the microphones and/or the consumer
electronics device, and to program at least a portion of the audio
parameters.
12. The tuning rig in accordance with claim 11, wherein the
workstation comprises and/or is configured to communicate with a
master design record, the master design record configured to output
a reference dataset, at least a portion of the audio parameters
depending on the reference dataset.
13. The tuning rig in accordance with claim 11, wherein the
workstation is configured to communicate one or more of the audio
test signals, one or more measured signals, and/or identification
information pertaining to the consumer electronics device to a
cloud based data center.
14. The tuning rig in accordance with claim 11, wherein the
workstation is configured to receive one or more audio enhancement
parameters from the cloud based data center and to program the
consumer electronics device with the audio enhancement
parameters.
15. The tuning rig in accordance with claim 11, wherein the
workstation comprises software for calculating one or more optimal
audio parameters from the audio test signals and the measured
signals, and for programming the optimal audio parameters onto the
consumer electronics device.
16. The tuning rig in accordance with claim 11, wherein the
acoustic test chamber is an anechoic chamber or semi-anechoic
chamber.
17. The tuning rig in accordance with claim 11, comprising an
system in accordance with claim 1.
18. A method for enhancing the audio performance of a consumer
electronics device comprising: measuring at least a portion of an
acoustic signature of the consumer electronics device; comparing
the portion of the acoustic signature of the consumer electronics
device to a master design record to produce one or more
reconfigured compensation parameters; and programming the
reconfigured compensation parameters onto the consumer electronics
device.
19. The method in accordance with claim 18, comprising placing the
consumer electronics device into an audio test chamber.
20. The method in accordance with claim 18, comprising programming
a system code along with the reconfigured compensation parameters
onto the consumer electronics device.
21. The method in accordance with claim 18, comprising deriving a
device profile from the consumer electronics device.
22. The method in accordance with claim 18, comprising sending the
acoustic signature, the device profile, and/or the reconfigured
compensation parameters to a cloud based data center.
23. The method in accordance with claim 18, comprising obtaining
the master design record from a cloud based data center.
24. A method for tuning the audio performance of a consumer
electronics device comprising: forming a master design record for
the consumer electronics device comprising a reference audio
parameter set and a reference audio test dataset; uploading the
reference audio parameter set to the consumer electronics device;
performing an audio test on the consumer electronics device to form
a test dataset; comparing the test dataset with the reference
dataset to form a new target acoustic response; generating a tuned
audio parameter set from the reference audio test data and the new
target acoustic response; and uploading the tuned audio parameter
set to the consumer electronics device.
25. The method in accordance with claim 24, wherein the step of
generating is completed with a system in accordance with claim
1.
26. The method in accordance with claim 24, wherein the step of
performing is completed with a tuning rig in accordance with claim
1.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application is a continuation of U.S. Pat. No.
9,386,386, filed Jul. 8, 2014, which is a national stage
application of PCT International Application No. PCT/US2013/020734,
filed Jan. 9, 2013, which claims benefit of and priority to U.S.
Provisional Application Ser. No. 61/584,462, filed Jan. 9, 2012,
the entire contents of each of which are incorporated by reference
herein.
BACKGROUND
[0002] 1. Technical Field
[0003] The present disclosure is directed to systems and methods
for improving audio output from consumer electronics devices. More
precisely, the present disclosure is directed towards systems and
methods for optimizing audio performance of a consumer electronics
device as part of a design and/or manufacturing process.
[0004] 2. Background
[0005] Mobile technologies and consumer electronics devices (CED)
continue to expand in use and scope throughout the world. In
parallel with continued proliferation, there is rapid technical
advance of device hardware and components, leading to increased
computing capability and incorporation of new peripherals onboard a
device along with reductions in device size, power consumption,
etc.
[0006] Audio experience is one of many factors considered in the
design of consumer electronics devices. Often, the quality of audio
systems, loudspeakers, etc. are compromised in favor of other
design factors such as cost, visual appeal, form factor, screen
real-estate, case material selection, hardware layout, and assembly
considerations amongst others.
[0007] Audio subassemblies and components, including loudspeakers,
connectors, filters, gaskets, waveguides, mounting hardware, and/or
drivers are generally fabricated and tested to specification by one
or more component suppliers and then assembled into consumer
electronics devices by a device assembly manufacturer. As such, by
the nature of this business practice, the audio subassemblies
include aspects such as self-contained speaker enclosures that may
add unnecessary material and size to the components.
Simultaneously, the design of such audio subassemblies may be
highly compromised due to the size and space limitations allotted
for the subassembly within a consumer electronics device.
[0008] In addition, part to part manufacturing variations as well
as production changes (e.g. in terms of component changes, device
revisions, process changes, etc.) may all have a significant, often
negative, impact on the audio performance of the consumer
electronics device. Thus a consumer may receive a device with
degraded performance. Alternatively, manufacturing delays and/or
reruns may be necessary to correct for the degraded device
performance before the device is launched and/or shipped.
[0009] The audio performance of a consumer electronics device may
be further impacted, often negatively, by the attachment of one or
more accessories thereto (e.g. a soft or hard holding case, a
scratch resistant cover sheet, a mounting unit, a stand, etc.).
Such changes in the audio performance of the device may limit the
usage cases available to a consumer and/or decrease the consumer
experience related thereto.
SUMMARY
[0010] One objective of this disclosure is to provide a system and
a method for enhancing audio performance of a consumer electronics
device during the design and/or manufacturing thereof.
[0011] Another objective is to provide a system and method for
tuning the audio performance of a consumer electronics device
during the manufacturing thereof, at the point of sale, and/or
after attachment of an accessory thereto.
[0012] Yet another objective is to provide a consumer electronics
device with enhanced audio performance derived from audio
components of reduced size, cost and/or complexity.
[0013] Another objective is to provide a manufacturing method and
system for semi- and/or fully automatic reduction of part to part
variation and/or optimization of the audio performance of a
consumer electronics device.
[0014] The above objectives are wholly or partially met by devices,
systems, and methods according to the appended claims in accordance
with the present disclosure. Features and aspects are set forth in
the appended claims, in the following description, and in the
annexed drawings in accordance with the present disclosure.
[0015] According to a first aspect there is provided, a system
(e.g. an optimization system) for optimizing the audio performance
of a consumer electronics device including an audio testing
component for deriving an audio test dataset from the consumer
electronics device, a master design record for outputting a
reference dataset, an audio parameter generator for deriving one or
more optimal audio parameters from the audio test dataset and the
reference dataset, and a programming unit to program the optimal
audio parameters onto the consumer electronics device.
[0016] In aspects, the system may include a probabilistic model for
determining the optimal audio parameters. Some non-limiting
examples of suitable probabilistic models include a Kalman filter,
a Markov model, a neural network, a Bayesian network, a fuzzy
network, a self-organizing map, a dynamic Bayesian network,
combinations thereof, and the like.
[0017] In aspects, the system may include a machine learning
algorithm for training the probabilistic model.
[0018] The master design record may be provided in accordance with
the present disclosure. In aspects, the master design record may
include at least a portion of a history of audio test datasets and
associated optimal audio parameters.
[0019] In aspects, the system may include an acoustic analysis unit
for generating a relative dataset from the reference dataset and
the audio test dataset. The audio parameter generator may be
configured to accept the relative dataset for use in generating the
optimal audio parameters.
[0020] In aspects, the acoustic analysis unit may include a feature
extraction block and/or a variance analysis block. The feature
extraction block may be configured to derive one or more audio
features from the audio test dataset and/or the reference dataset,
the audio features included in the relative dataset. The variance
analysis block may be configured to derive an audio variance
dataset from the audio test dataset and the reference dataset, the
audio variance dataset included in the relative dataset.
[0021] In aspects, the system may include a manual parameter
building interface comprising a display and a data input device for
interfacing with a human user.
[0022] According to another aspect there is provided, a tuning rig
for optimizing the acoustic performance of a consumer electronics
device configured to accept one or more programmable audio
parameters including an acoustic test chamber configured to accept
the consumer electronics device, one or more microphones placed
within the acoustic test chamber, and a workstation. The
workstation may be configured in operable communication with the
consumer electronics device and the microphones, to deliver one or
more audio test signals to the consumer electronics device, receive
one or more measured signals from the microphones and/or the
consumer electronics device, and/or to program at least a portion
of the audio parameters.
[0023] In aspects, the workstation may include and/or be configured
to communicate with a master design record in accordance with the
present disclosure. The master design record may be configured to
output a reference dataset. At least a portion of the audio
parameters may depend upon the reference dataset.
[0024] The workstation may be configured to communicate with a
cloud based data center. The workstation may communicate such
information as the audio test signals, one or more measured
signals, audio enhancement parameters, and/or identification
information pertaining to the consumer electronics device to and or
receive such information from a cloud based data center. In
aspects, the master design record may be included the cloud based
data center, within a remote computing service network, or the
like.
[0025] In aspects, the workstation may include software for
calculating one or more substantially optimal audio parameters from
the audio test signals and the measured signals, and for
programming the optimal audio parameters onto the consumer
electronics device.
[0026] In aspects, the acoustic test chamber may be an anechoic
chamber or semi-anechoic chamber.
[0027] In aspects, the tuning rig may include a system (e.g. an
optimization system) in accordance with the present disclosure.
[0028] According to yet another aspect there is provided, use of a
tuning rig in accordance with the present disclosure in a
manufacturing process.
[0029] According to another aspect there is provided, use of a
tuning rig in accordance with the present disclosure in a retail
store, a device repair setting, a case distributor, or the
like.
[0030] According to yet another aspect there is provided, a method
for enhancing the audio performance of a consumer electronics
device including, measuring at least a portion of an acoustic
signature of the consumer electronics device, comparing the portion
of the acoustic signature of the consumer electronics device to a
master design record to produce one or more reconfigured
compensation parameters, and programming the reconfigured
compensation parameters onto the consumer electronics device.
[0031] The method may include placing the consumer electronics
device into an audio test chamber, programming a system code along
with the reconfigured compensation parameters onto the consumer
electronics device, and/or deriving a device profile from the
consumer electronics device.
[0032] The method may include sending the acoustic signature, the
device profile, and/or the reconfigured compensation parameters to
a cloud based data center and/or obtaining associated information
and/or a master design record from the cloud based data center.
[0033] According to another aspect there is provided, a method for
tuning the audio performance of a consumer electronics device
including, forming a master design record for the consumer
electronics device comprising a reference audio parameter set and a
reference audio test dataset, uploading the reference audio
parameter set to the consumer electronics device, performing an
audio test on the consumer electronics device to form a test
dataset, comparing the test dataset with the reference dataset to
form a new target acoustic response, generating a tuned audio
parameter set from the reference audio test data and the new target
acoustic response, and uploading the tuned audio parameter set to
the consumer electronics device.
[0034] The step of generating may be completed with a system in
accordance with the present disclosure.
[0035] The step of performing may be completed with a tuning rig in
accordance with the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] FIG. 1 shows a schematic of an optimization system in
accordance with the present disclosure for tuning and/or optimizing
one or more audio parameters of a consumer electronics device.
[0037] FIG. 2 shows a schematic of an optimization system in
accordance with the present disclosure for tuning and/or optimizing
one or more audio parameters of a consumer electronics device.
[0038] FIGS. 3a-c show schematics of aspects of an optimization
system in accordance with the present disclosure.
[0039] FIG. 4 shows an illustration of an audio performance space
and an associated audio parameter space in accordance with the
present disclosure.
[0040] FIG. 5 shows an audio enhancement system in accordance with
the present disclosure.
[0041] FIGS. 6a-b show a consumer electronics device and audio
spectral responses obtained therefrom.
[0042] FIGS. 7a-b show methods for optimizing audio performance of
a consumer electronics device in accordance with the present
disclosure for use during a design phase and/or a manufacturing
process of the consumer electronics device.
[0043] FIG. 8 shows a method for optimizing audio performance of a
consumer electronics device including an integrated loudspeaker
system and an audio enhancement system in accordance with the
present disclosure.
[0044] FIG. 9 shows a tuning rig for testing, validating,
programming, and/or updating an audio enhancement system within a
consumer electronics device in accordance with the present
disclosure.
DETAILED DESCRIPTION
[0045] Particular embodiments of the present disclosure are
described hereinbelow with reference to the accompanying drawings;
however, the disclosed embodiments are merely examples of the
disclosure and may be embodied in various forms. Well-known
functions or constructions are not described in detail to avoid
obscuring the present disclosure in unnecessary detail. Therefore,
specific structural and functional details disclosed herein are not
to be interpreted as limiting, but merely as a basis for the claims
and as a representative basis for teaching one skilled in the art
to variously employ the present disclosure in virtually any
appropriately detailed structure. Like reference numerals may refer
to similar or identical elements throughout the description of the
figures.
[0046] By consumer electronics device (CED) is meant a cellular
phone (e.g. a smartphone), a tablet computer, a laptop computer, a
portable media player, a television, a portable gaming device, a
gaming console, a gaming controller, a remote control, an appliance
(e.g. a toaster, a refrigerator, a bread maker, a microwave, a
vacuum cleaner, etc.) a power tool (a drill, a blender, etc.), a
robot (e.g. an autonomous cleaning robot, a care giving robot,
etc.), a toy (e.g. a doll, a figurine, a construction set, a
tractor, etc.), a greeting card, a home entertainment system, an
active loudspeaker, a soundbar, or the like.
[0047] The consumer electronics device (CED) may include an audio
enhancement system (AES) in accordance with the present
disclosure.
[0048] All consumer electronic devices have an inherent acoustic
signature as described below. An associated audio enhancement
system (AES) may be configured to compensate for this acoustic
signature to enhance and/or standardize the audio output from the
device. In the case of the consumer electronic device being an
appliance or a power tool, the audio enhancement system may be
configured to cancel operating noise, augment operating noise,
provide alerts to a user, etc. The audio enhancement system may be
configured as an all-digital implementation, which may be suitable
for lowering system cost, specifically in terms of the processor,
but also in terms of using lower cost transducers, reducing power
requirements, etc. The audio enhancement system may also be
configured to maintain acceptable audio performance in a low cost
application when paired with an exceedingly low cost transducer. In
the case of a mobile or battery operated consumer electronic
device, such as a portable gaming device, the audio enhancement
system may be configured to enhance the audio experience for the
user while minimizing power usage, thus extending the battery life,
reducing onboard heat generation, etc.
[0049] By transducer 540, 560 is meant a component or device such
as a loudspeaker suitable for producing sound. A transducer 540,
560 may be based on one of many different technologies such as
electromagnetic, thermoacoustic, electrostatic, magnetostrictive,
ribbon, audio arrays, electroactive materials, exciters, and the
like. Transducers 540, 560 based on different technologies may
require alternative driver characteristics, matching or filtering
circuits but such aspects are not meant to alter the scope of this
disclosure.
[0050] By transducer module 550 is meant a subsystem including both
a transducer 560 and a circuit 555. The circuit 555 may provide
additional functionality (e.g. power amplification, energy
conversion, filtering, energy storage, etc.) to enable a driver
external to the transducer module 550 to drive the transducer 560.
Some non-limiting examples of the circuit 555 include a passive
filter circuit, an amplifier, a de-multiplexer, a switch array, a
serial communication circuit, a parallel communication circuit, a
FIFO communication circuit, a charge accumulator circuit,
combinations thereof, and the like.
[0051] By input audio signal 501 is meant one or more signals (e.g.
a digital signal, one or more analog signals, a 5.1 surround sound
signal, an audio playback stream, etc.) provided by an audio source
(e.g. a processor, an audio streaming device, an audio feedback
device, a wireless transceiver, an ADC, an audio decoder circuit, a
DSP, etc.).
[0052] By acoustic signature is meant the audible or measurable
sound characteristics of a consumer electronic device dictated by
its design and/or manufacturing processes, process variations, etc.
that influence the sound generated by the consumer electronic
device. The acoustic signature is influenced by many factors
including the loudspeaker design (speaker size, internal speaker
elements, material selection, placement, mounting, covers, etc.),
device form factor, internal component placement, screen
real-estate and material makeup, case material selection, hardware
layout, manufacturing process variations, manufacturing component
changes, manufacturing process changes, and assembly considerations
amongst others. Cost reduction, form factor constraints, visual
appeal and many other competing factors may be favored during the
design process at the expense of the audio quality of the consumer
electronic device. Thus the acoustic signature of the device may
deviate significantly from an idealized response (e.g. a target
acoustic response).
[0053] Manufacturing variations in the above aspects may
significantly influence the acoustic signature of each device,
causing further part to part variations that may degrade the audio
experience for a user. Some non-limiting examples of factors that
may affect the acoustic signature of a consumer electronic device
include: insufficient speaker size, which may limit movement of air
necessary to re-create low frequencies, insufficient space for the
acoustic enclosure behind the membrane which may lead to a higher
natural roll-off frequency in the low end of the audio spectrum,
insufficient amplifier power available, an indirect audio path
between membrane and listener due to speaker placement often being
on the back of a TV or under a laptop, relying on reflection to
reach the listener, among others factors.
[0054] In aspects, the acoustic aspects of the loudspeaker 540, 560
may be significantly altered and influenced by the casing of the
CED, the portion of the enclosure of the CED available as a back
volume, the number, placement and/or organization of other
components within the CED, the mounting aspects of the loudspeaker,
and the like. Many of the acoustic aspects of such systems may not
be fully characterize able until the system has been completely
assembled. Even seemingly minor process variations may
significantly influence the acoustic performance of the CED.
[0055] In aspects, the acoustic performance of the CED may be
significantly altered by the attachment of an accessory (e.g. a
casing, a screen protector, a jacket, a mounting clip, a stand,
etc.).
[0056] Thus an AES in accordance with the present disclosure may be
optimized late in the design process, during the development
process, in the field, at a retail outlet, and/or during a
manufacturing process to compensate for one or more of these,
generally negative, influences on the acoustic properties in the
fully manufactured device.
[0057] In aspects, the AES may be configured initially during the
design stage of the product development, in an audio test facility.
Thus an initial set of AES parameters may be assembled and loaded
into the AES. During manufacturing of the CED, individual devices,
batches, etc. may have differing acoustic properties and anomalies
due to manufacturing variances, component changes, material
changes, etc. Tuning and/or optimization of the AES included in
each device may be used to adjust the audio performance of the
device during the manufacturing process. The tuning and/or
optimization of the AES, and/or associated parameters related to a
particular CED may be performed with an optimization system 101 in
accordance with the present disclosure.
[0058] FIG. 1 shows a schematic of an optimization system 101 in
accordance with the present disclosure for tuning and/or optimizing
one or more audio parameters of a consumer electronics device. The
optimization system 101 includes an audio testing component 110
configured for gathering acoustic data 105 from a sample CED 11, a
master design record 120 configured to store and for providing data
125 to other components of the system 101, and an audio enhancement
system (AES) parameter generator 160 configured to produce one or
more AES parameters 170 from the provided data 115, 125. The AES
parameters 170 may be downloaded to an associated AES 500 in
accordance with the present disclosure, to a batch of manufactured
CEDs, etc.
[0059] In aspects, the AES parameters 170 may be directed 175 to
the audio testing component 110 and/or the sample CED 11 for
further testing. In this sense, the process of generating a final
set of AES parameters 170 for a sample CED 11 may be iteratively
implemented within the optimization system 101.
[0060] In aspects, the audio testing component 110 may produce
audio data 115 for use by the AES parameter generator 160, for
contribution and/or comparison to the master design record 120,
etc. The audio testing component 110 may be implemented as part of
a tuning rig 900 in accordance with the present disclosure. Thus
the audio testing component 110 may be implemented in code and/or
hardware for carrying out the steps of determining parameters 704,
802 and/or the step of analyzing the CED 714 in accordance with the
present disclosure.
[0061] The master design record 120 may include acoustic reference
datasets, population datasets associated with the product family of
the CED 11, master records of audio enhancement parameters, audio
performance characteristics, reference datasets, master records
associated with audio test datasets (e.g. audio test data input and
output information) collected from representative CEDs, etc. The
master design record 120 may be configured to output data 125 for
use in the system 101 and to accept test data 115, 125 from other
blocks in the system 101 for use in building and/or augmenting a
dataset. Recorded test data 115, 125 may be validated and stored
within the master design record 120 for future reference.
[0062] In one non-limiting example, the CED acoustic optimization
process may be split into multiple steps. A master design record
120 may be constructed for a particular product line during the
product design process. Such a master design record 120 may be
constructed using a complete suite of audio testing equipment and
analytics, in a highly controlled and state of the art testing
facility. Such a process, optionally to a lesser degree of
precision, may also be performed on sample batches and/or
individual devices during the product design and production ramp-up
phases of a product line in order to generate statistical models
relating to the master design record 120 that are characteristic of
the CED for the product line. Such models may be advantageous for
determining the relationship between process variations and
associated variations in the audio performance of each manufactured
device. Any portion of this data, relevant to the product line may
be stored within and made available by a master design record in
accordance with the present disclosure.
[0063] The master design record 120 may be further developed during
the design and manufacturing of a CED so as to improve product
yield, reduce part to part variation, ensure optimal audio
performance, etc. as process and product changes associated with
the manufacture of the CED change.
[0064] The AES parameter generator 160 may implement one or more
methods for calculating a set of AES parameters from test data 115
and/or data 125 from the master design record 120. The AES
parameter generator 160 may implement the formulation methods 706,
716 and/or the optimization method 804 in accordance with the
present disclosure.
[0065] By optimizing the AES for batches of devices and sample
devices during the design and manufacturing ramp-up phases of a
consumer electronics device (CED), a design space for the AES
parameters as they relate to process variations may be formulated.
Thus the relationship between optimal AES parameter variations and
audio performance variants due to variations in manufacturing
processes may be semi-automatically compensated for during the
manufacturing ramp-up and full manufacturing of the product line.
Early established relationships, as well as validated changes in
such relationships (perhaps as occur during refinement of a
manufacturing process, etc.), may be integrated into the master
design record 120 and provided as data 125 during the parameter
generation process.
[0066] In one non-limiting example, a master design record in
accordance with the present disclosure may be used to tune the
audio performance of a consumer electronics device. The master
design record may include a reference audio parameter set and a
reference audio test dataset. The reference audio parameter set may
be an optimal parameter set obtained from a reference design of the
consumer electronics device, perhaps during the design process of
the device, during the ramp-up phase of the development, from a
manufactured batch of devices, etc. The reference audio parameter
set may pertain to an audio parameter set that best matches the
acoustic response of the reference design, when subjected to a
series of audio tests, to a target and/or ideal acoustic
response.
[0067] The reference audio parameter set may be uploaded to the
consumer electronics device and the consumer electronics device may
then be subjected to a series of audio tests to form a test
dataset. The test dataset may then be compared with the reference
dataset, the difference between the data sets being used to form a
new target acoustic response. The new target acoustic response may
then be used in conjunction with the reference audio test data to
generate a tuned audio parameter set. The tuned audio parameter
dataset may then be uploaded to the consumer electronics device.
One or more steps in this process may be achieved using an
optimization system 101, 201 and/or a tuning rig 900 both in
accordance with the present disclosure.
[0068] The target acoustic response may be a Dirac delta function,
a realistically achievable approximation of a Dirac delta function,
a predetermined frequency response, an output associated with an
audio data stream (e.g. a song, a chirp, etc.), or the like.
[0069] The new target acoustic response, may take on a form that is
considerably similar to the target acoustic response, but may vary
from that response in terms of frequency dependent aspects,
features, etc. In one non-limiting example, the optimization system
101, 201 may determine the differences (e.g. variance within each
octave band, differences between integration of impulse responses,
etc.) between the new target acoustic response and the target
acoustic response to form an audio variance dataset. If the
magnitude of key parameters within the variance dataset are within
an acceptable margin (e.g. .+-.1 dB within each octave band
variance) the optimization system 101, 201 may opt not to update
the audio parameter set, if the variance dataset is outside of the
margin, the optimization system 101, 201 may opt to derive a tuned
audio parameter set for upload to the consumer electronics
device.
[0070] FIG. 2 shows a schematic of an adaptive optimization system
201 configured to tune and/or optimize one or more audio parameters
of a consumer electronics device 10, 11, 610. The adaptive
optimization system 201 includes an audio testing component 210, a
master design record 220, a parameter generation model 260, and a
machine learning algorithm 240, configured to train the parameter
generation model 260. The system 201 may include an acoustic
analysis unit 230 configured to provide features, differences, data
225, and/or metrics (e.g. collectively referred to as relative data
235) derived via comparison between data 225 from the master design
record 220 and the test data 215 to other aspects of the adaptive
optimization system 201.
[0071] The parameter generation model 260 may be configured to
accept relative data 235 as well as influence 255 from the machine
learning algorithm 240 to produce the AES parameters 270.
Optionally, the substantially optimal AES parameters 270 may be
arrived at iteratively. In aspects, the AES parameters 270 may be
directed 275 to the audio testing component 210 for further use
during each iteration of the testing procedure.
[0072] The parameter generation model 260 may be implemented via a
deterministic, probabilistic and/or combination model. A
probabilistic model may be used to link AES parameters and
parameter variations due to changes in the audio performance of the
batch samples, further elucidating and establishing design
relationships between product performance variance and AES
parameters. Some suitable models may include Kalman filters, Markov
models, back propagation artificial neural networks, Bayesian
networks, basis functions, support vector machines, stochastic
modeling methods (e.g. Monty Carlo methods, multilevel models,
hierarchical models, nested models, mixed models, random
coefficient, random-effects models, random parameter models, etc.),
Gaussian process regression, information fuzzy networks, regression
analysis, self-organizing maps, logistic regression, time series
models such as autoregression models, dynamic Bayesian networks,
moving average models, autoregressive integrated moving average
models, classification and regression trees, multivariate adaptive
regression splines, combinations thereof, and the like.
[0073] The machine learning algorithm 240 may be configured to
accept user input 245 from a human expert 12. The human expert 12
may provide manual training assistance and acoustic performance/AES
parameter match confirmations early in the development of the CED.
Such input may be used to develop an automatic or semi-automatic
learning algorithm for calculating future incidences of acoustic
parameters (e.g. AES parameters). Some non-limiting examples of
learning algorithms include non-linear least squares, L2 norm,
averaged one-dependence estimators (AODE), case-based reasoning,
decision trees, regression analysis, self-organizing maps, logistic
regression, and the like.
[0074] The human expert 12 may help to train the adaptive system
201. Alternatively, additionally, or in combination the human
expert 12 may execute manual parameter builds and tests so as to
build the overall structure of the parameter generation model 260
and/or master design record 220 before handing control over to the
machine learning algorithm 240 for more automated testing and
optimization of AES parameters 270.
[0075] The optimization system 101, 201 may be implemented in a
workstation 960 or equivalently in a cloud data center. The
optimization system 101, 201 may include algorithms to compare
audio performance histories of manufactured CEDs 10 and trends in
the datasets (e.g. as provided by the master design record 120,
220) suitable to predict the performance criteria for the present
batch of manufactured consumer electronics devices based on the
testing and optimization results of a tested CED 10. Such a
configuration may be advantageous for economically optimizing the
audio performance of a batch of consumer electronics devices during
the manufacturing process without having to test and optimize every
unit that is manufactured. In one non-limiting example, such
functionality may be provided by an optimization system 101, 201
and/or with a tuning rig 900 in accordance with the present
disclosure.
[0076] In one non-limiting example, the audio enhancement
parameters 170, 270 for the CED 10 may be saved within the cloud in
the form of a device profile. The device profile may contain
identification information, manufacturing tracking information,
acoustic performance data, usage data, audio enhancement
parameters, tuned AES parameters, and/or the like, such information
being a unique identifier of the consumer electronics device. In
one non-limiting usage example, an audio streaming service may use
the device profile to pre-process an audio stream before sending
the audio stream to the CED 10. Such a configuration may be
advantageous for improving audio output from the CED 10 while
simultaneously minimizing the power consumed on the CED 10 during
use.
[0077] FIGS. 3a-c show schematics of aspects of an optimization
system 101, 201 in accordance with the present disclosure. FIG. 3a
shows an audio test unit 110, 210 including optionally an AES
parameter integration block 310, a thorough audio testing suite
320, and a truncated audio testing suite 330. The audio test unit
110, 210 may be configured to accept audio data 105, 205 from a
sample CED 11. The AES parameter integration block 310 may be
configured to simulate and/or to download AES parameters 175, 275
to the CED 11 such that the AES affected output 315 can be
generated and provided to other units in the optimization system
101, 201 via implementation of one or more test procedures. With or
without the AES parameter integration block 310, the thorough audio
testing suite 320 may include a range of tests to perform on the
CED to extract the necessary audio information from the CED 11 for
use in determining a substantially optimal set of AES parameters
175, 275 for use on the CED 11 (i.e. optimized so as to enhance the
audio output capability of the associated consumer electronics
device 11). A thorough audio test suite may include a range of
audio tests (e.g. impulse signals, frequency sweeps, music clips,
pseudo-random data streams, etc.). In addition, the tests may be
performed with sufficient detail (e.g. with higher precision, more
accurate inputs, etc.) as well as with higher tech equipment during
implementation of the thorough test suite 320. Alternatively, the
truncated audio test suite may perform only a subset of tests (e.g.
a music clip, a frequency sweep etc.), optionally with less scope
and/or precision (i.e. so as to reduce test times, to characterize
key features in the acoustic response, etc.). In aspects, the test
datasets 115, 215 may be made available to other components in the
optimization system 101, 201.
[0078] In aspects, a thorough test suite 320 may be implemented
during the design and manufacturing ramp-up of a CED, optionally in
order to build at least a portion of a master design record, to
train a learning algorithm, to tune a parameter generation model,
or the like.
[0079] In aspects, a truncated test suite 330 may be implemented
during manufacturing, at the point of sale, etc. in order to
determine various acoustic aspects (e.g. a resonant peak, a
resonant frequency, a bass response, a phase delay, a defect, etc.)
for use in the tuning of an AES parameter set for use with the
CED.
[0080] Differences between the thorough test suite 320 and the
truncated test suite 330 may be characterized and stored so as to
compensate for these differences during an optimization process on
an individual device. One non-limiting method for characterizing
the differences may be to perform the test suites 320, 330 on the
same reference consumer electronics device, and/or in a high
quality anechoic chamber, versus a lower quality acoustic test
chamber, etc.
[0081] FIG. 3b shows a schematic of a machine learning algorithm
240 including a learning algorithm 360 and optionally a manual
parameter building interface 350. The learning algorithm 360 may be
configured to provide an influence 255 in order to build, adapt
and/or tune the parameter generation model automatically,
semi-automatically and/or with expert assistance. The manual
parameter building interface 350 may be configured to provide a
training signal 355 to the learning algorithm 360. The training
signal 355 may be dependent on expert input 245, the relative data
235, etc.
[0082] The parameter building interface 350 may be configured to
accept data 125, 115, 235 from the master design record 120, 220
the audio testing unit 110, 210, and/or the acoustic analysis unit
230 as well as to accept inputs 245 from the audio domain expert
12.
[0083] In aspects, the manual parameter building interface 350 may
be used to build and/or validate relationships between audio
performance data and the AES parameters in the form of models
within the parameter generation model 260 through, optionally
interfacing with an audio domain expert 12.
[0084] FIG. 3c shows a schematic of an optional acoustic analysis
unit 230 including a feature extraction block 380 and a variance
analysis block 390. The feature extraction block 380 may be used to
extract quantitative and/or qualitative acoustic features (e.g. a
resonant peak, a resonant frequency, a bass response, a phase
delay, a defect, general features, feature set categories, etc.)
from test data 115 and master design record data 125. The extracted
features, variance, test data, and/or master design record data
(collectively referred to as relative data 235) may be provided to
the AES generation model 260 and/or the machine learning algorithm
240.
[0085] The variance analysis block 390 may be configured to
generate metrics relating deviations between the CED audio
parameter population, the master design record 220, manufacturing
batch properties, or the like with test data 215 related to the
sample CED 11 under test.
[0086] FIG. 4 shows an illustration of an audio performance space
405 and an associated audio parameter space 406 in accordance with
the present disclosure. A two dimensional relationship is shown
only so as to highlight the concepts and relationships relevant to
the present disclosure. In practice, higher order spaces may be
necessary to characterize the audio performance space 405 and
associated parameter space 406. The arbitrary axes shown may relate
to quantitative aspects of a key acoustic feature (e.g. a resonant
peak, a resonant frequency, a bass response cut-off frequency, an
integrated frequency response, etc.). Thus a measured instant on
the graph (as indicated by an X), may be related to a measured
response in terms of a key acoustic feature. Relating to higher
order (and thus more practical spaces), all key peaks, etc. may be
used as inputs to the model. Alternatively, raw data may be used as
a model input against which variance and feature extraction metrics
may be measured. The associated audio parameter space 406 may have
arbitrary axes relating to acoustic parameters, model response,
aspects related to a target acoustic response (e.g. as stored in
the master design record), etc.
[0087] Qualitative aspects of the acoustic response may be used to
further classify the overall behavior of the CED (e.g. so as to
direct the model to alternative AES structures, introduce new
parameters and/or AES components in response to a detected acoustic
feature, etc.).
[0088] An audio performance point in the audio performance space
405 may relate to a point in the associated AES parameter space 406
(e.g. point to point relationship 470). Small changes in response
about an audio performance point in the audio performance space 405
may be associated with variance of the AES parameters in the
associated AES parameter space 406 (e.g. variability relationships
480).
[0089] As shown an ideal design point 410 and associated ideal
parameters 411 may be determined during the design of the CED and
associated AES. An acceptable parameter variance boundary 420, 421
may be fixed such that any test sample that produces a test point
(designated X) within the acceptable boundary 420, 421, may perform
adequately with the ideal parameters 411 (i.e. thus no AES updates
may be necessary).
[0090] An automatically recoverable boundary 430, 431 may be
defined such that a robust automatic adjustment of the AES
parameters may be ensured if the test point (e.g. test point 415,
416) is provided within this boundary 430, 431. Such automatic
adjustment may be provided by an optimization system 101, 201 in
accordance with the present disclosure.
[0091] There may also be manually recoverable regions 440, 441 in
the audio performance space 405, wherein an expert manual
optimization and/or thorough testing regiment may be required in
order to suitably adjust the AES parameters for the sample CED 11.
Such manually recoverable regions 440, 441 may appear routinely
during the training of the optimization system 101, 201 or during
the product development of a CED. As the optimization system 101,
201 is built and the relationships between optimal AES parameters
and samples occurring in the manually recoverable regions 440, 441
become established, the automatically recoverable boundary 430, 431
may be expanded to encase those regions (thus no longer requiring
deeper intervention in order to tune the associated AES
parameters). Such changes may be suitable ways to build and train
the optimization system 101, 201 for use with a new CED product
family.
[0092] There may also be unrecoverable regions 450, 451 (e.g.
associated with manufacturing faults, unstable assembly conditions,
faulty components, etc.) which may be monitored as part of a
quality control system.
[0093] As swaths of samples are tested by such an approach, clear
relationships between particular test batches of product and the
optimal AES configuration may be established. During such testing,
the boundaries 410, 411, 420, 421, 430, 431 may grow in both size
and robustness. Thus the optimal system 101, 201 may better
optimize the AES parameters for a given response as measured from a
sample CED 11.
[0094] Such robust characterization and relationships may provide a
means for detecting measurement errors that may happen in a
manufacturing environment. Thus if a sample CED test results in a
test point outside of a known boundary 410, 411, 420, 421, 430, 431
the optimization system may retest the sample CED to determine if a
measurement error occurred. Such error detection may be
advantageous for improving production line robustness of such a
process. Qualitative features may be further used to assist in the
detection of such measurement and/or device errors.
[0095] In aspects, during a training/learning process, as the
relationships between process variation and AES model properties
become better defined and predictable, fewer and fewer batch
samples may need be tested from manufacturing batches in order to
calculate and/or select AES parameters for implementation into a
particular device or batch of devices.
[0096] Furthermore, as the relationships become more clearly
defined during manufacturing ramp-up, the breadth and depth of the
batch testing may be shortened and simplified considerably so as to
only hone in on relevant property variations that constitute
alternative AES parameter configurations (i.e. the appearance of a
new acoustic feature). Other changes may be automatically
compensated for by a trained optimization system 201 in accordance
with the present disclosure.
[0097] Such an approach may further be used to track manufacturing
property drift in the product line. If drift is detected, larger
samples maybe drawn from the manufactured lots for a period of time
in order to establish new relationships between the recently
manufactured products and their associated optimal AES parameters.
As the new relationships are better understood, batch sampling may
be reduced back to a minimal level.
[0098] Criteria for adjusting the AES parameters may also be
established during early stage testing of a product line. The
criteria may define an acceptable variance between performance of a
manufacturing test sample and the master design record beyond which
adjustments to the AES parameters may be made.
[0099] In aspects, the AES parameters may be broken into a batch
sample master parameter set and a subset of parameters that may be
tweaked during a rapid manufacturing test (e.g. a subset of biquad
filters and/or equalizers that may be used to pull the AES in a
particular direction to accommodate manufacturing process
variations). The configurable subset of parameters may be
established with relation to the known manufacturing variances for
a product line, such that the properties of the AES are not overly
changed during a revision, and/or to establish measureable ranges
in the parameters that may be expected during manufacturing (e.g.
to determine if a measurement error occurred during a rapid
manufacturing test). Such configurations may be suitable to
maintain stable and high quality operation of the devices amid a
high degree of manufacturing process variation.
[0100] In one non-limiting example, a sample consumer electronics
device is tested in accordance with the present disclosure less
than every 10,000 units, every 1,000 units, every 100 units, or
every 10 units during a production run. If manufacturing variations
necessitate changes in an AES parameter set, detailed testing of
the samples could be used to establish new optimal parameters for
the AES production population.
[0101] The audio enhancement system (AES) may be programmed with
audio parameters (e.g. pre-configurable and/or reconfigurable
parameters, optimal audio parameters, etc.) at the time of acoustic
testing, along with programming of the other components in the CED
(e.g. during JTAG programming of the CED chipsets, etc.), or the
like.
[0102] In one non-limiting example, the CED may include a separate
audio input/programming port accessible to the test chamber or
programmer to deliver the audio test signals and/or final audio
parameters to the CED during the manufacturing process.
[0103] In aspects, an AES in accordance with the present disclosure
may be preprogramed in a test mode, when powered on the AES may run
through a series of pre-programmed test procedures, record acoustic
feedback from the tests and upload the resulting information to a
test system (e.g. a workstation 260, etc.). The AES may then be
reprogrammed with the operational code (e.g. including the
optimized audio parameters). The reprogramming may occur along with
the rest of the CED chipsets, during JTAG testing, immediately
after the audio testing, etc.
[0104] FIG. 5 shows a schematic of an audio enhancement system in
accordance with the present disclosure. The audio enhancement
system 500 may be configured to accept one or more input audio
signals 501 from a source (e.g. a processor, an audio streaming
device, an audio feedback device, a wireless transceiver, an ADC,
an audio decoder circuit, a DSP, etc.), and to provide one or more
output signals 535 to one or more transducers 540 (e.g. a
loudspeaker, etc.), or transducer modules 550 (e.g. a transducer
560 combined with associated integrated circuits 555, etc.). The
audio enhancement system 500 may include internal blocks (e.g.
parametrically configurable processing [PCP] block, digital driver
[DD] block, asynchronous sample rate converter [ASRC] block, etc.)
which may be configured to transform and/or act upon the input
audio signal 1 or signals derived therefrom to produce the output
signal(s) 535.
[0105] In aspects, the audio enhancement system 500 may be provided
in software, embedded in an application specific integrated circuit
(ASIC), or be provided as a hardware descriptive language block
(e.g. VHDL, Verilog, etc.) for integration into a system on chip
integrated circuit (ASIC), a field programmable gate array (FPGA),
or a digital signal processor (DSP) integrated circuit. One or more
blocks (e.g. PCP block, ASRC block, etc.) may also be implemented
in software on the consumer electronics device and/or in an
associated network (e.g. a local network server, in the cloud,
etc.). In aspects, the AES 500 may be an all-digital hardware
implementation. An all-digital implementation may be advantageous
to reduce the hardware footprint, reduce power consumption, reduce
production costs, and increase the number of integrated circuit
processes into which the system may be implemented. The
implementation may be integrated into a consumer electronics device
in order to provide a complete audio enhancement solution.
[0106] As shown in FIG. 5, the audio enhancement system 500 for use
in a consumer electronics device may include a parametrically
configurable processing (PCP) block 520 and a digital driver (DD)
block 530. The audio enhancement system 500 may be configured to
accept one or more audio input signals 501 from an audio source. In
the schematic shown, the PCP block 520 may be configured to accept
the input signal 1 and to produce an enhanced signal 525. The
enhanced signal 525 may be directed to the DD block 530 which may
be configured to convert the enhanced signal 525 into one or more
output signals 535, suitable for driving a transducer 540 (e.g. a
loudspeaker, a speaker unit, a loudspeaker assembly, etc.) or a
transducer module 550.
[0107] The PCP block 520 may be configured to provide such
functions as FIR filtering, IIR filtering, warped FIR filtering,
transducer artifact removal, disturbance rejection, user specific
acoustic enhancements, user safety functions, emotive algorithms,
psychoacoustic enhancement, signal shaping, single or multi-band
compression, expanders or limiters, watermark superposition,
spectral contrast enhancement, spectral widening, frequency
masking, quantization noise removal, power supply rejection,
crossovers, equalization, amplification, driver range extenders,
power optimization, linear or non-linear feedback or feed-forward
control systems, and the like. The PCP block 520 may include one or
more of the above functions, either independently or in
combination. One or more of the included functions may be
configured to depend on one or more pre-configurable and/or
reconfigurable parameters 510.
[0108] The PCP block 520 may be configured to provide echo
cancellation, environmental artifact correction, reverb reduction,
beam forming, auto calibration, stereo widening, virtual surround
sound, virtual center speaker, virtual sub-woofer (by digital bass
enhancement techniques), noise suppression, sound effects, or the
like. One or more of the included functions may be configured to
depend on one or more of the parameters.
[0109] The PCP block 520 may be configured to impose ambient sound
effects onto an audio signal 501, such as by transforming the audio
input signal 501 with an ambient environmental characteristic (e.g.
adjusting reverb, echo, etc.) and/or superimposing ambient sound
effects onto the audio input signal 501 akin to an environmental
setting (e.g. a live event, an outdoor setting, a concert hall, a
church, a club, a jungle, a shopping mall, a conference setting, an
elevator, a conflict zone, an airplane cockpit, a department store
radio network, etc.).
[0110] The ambient sound effects may include specific information
about a user, such as name, preferences, etc. The ambient sound
effects may be used to securely superimpose personalized
information (e.g. greetings, product specific information,
directions, watermarks, handshakes, etc.) into an audio stream.
[0111] The DD block 530 may include a pulse width modulator (PWM).
The DD block 530 may be pre-configured and/or pre-selected to drive
a range of electroacoustic transducers (e.g. electromagnetic,
thermoacoustic, electrostatic, magnetostrictive, ribbon, arrays,
electroactive material transducers, etc.). The DD block 530 may be
configured to provide a power efficient PWM signal to the
transducer 540 or the input of a transducer module 550 (e.g. a
passive filter circuit, an amplifier, a de-multiplexer, a switch
array, a FIFO communication circuit, a charge accumulator circuit,
etc.).
[0112] In aspects, one or more block in the AES 500 (or the system
itself) may include pre-configurable and/or reconfigurable
parameters 510 suitable for configuring the audio processing
aspects of the AES 500 (e.g. signal conversion aspects, signal
processing aspects, system property compensation, etc.). In
aspects, the parameters 510 may be integrated into the AES in
general 500, for use by any block 520, 830 within the AES 500.
Alternatively or in combination, one or more parameters 510 may be
located externally to the AES 500, and the AES 500 may be
configured to accept one or more of the external parameters for use
by one or more blocks 520, 530 within the AES 500.
[0113] The pre-configurable and/or reconfigurable parameters 510
may be pre-configured during the design, manufacturing, validation,
and/or testing process of the consumer electronics device 10, 610.
Alternatively, additionally, or in combination, the parameters 510
may be pre-configured, tweaked and/or optimized during the
manufacturing, quality control, at the time of sale, during first
boot, during a boot sequence, and/or during a testing process of
the consumer electronics device 10, 610 (e.g. with an optimization
system 101, 201 and/or a tuning rig 900 both in accordance with the
present disclosure, in an audio test facility, in simulation,
etc.). Alternatively, additionally, or in combination, the
parameters 510 may be uploaded to the consumer electronics device
10, 610 during a firmware upgrade or through a software updating
process, or the like.
[0114] In aspects, one or more of the parameters 510 may be
dependent on the particular design of the consumer electronics
device 10, 11, 610 into which the AES 500 may be integrated and/or
to which the AES 500 may be interfaced. In aspects, one or more of
the parameters 510 may be dependent on the quality of audio
drivers, properties of an associated integrated loudspeaker
assembly in accordance with the present disclosure, the back volume
formed within the CED 10, component layout, loudspeakers, material
and assembly considerations, the casing of the consumer electronics
device 10, 11, 610, combinations thereof, or the like, for a
specific consumer electronics device, brand of device, or product
family of devices (e.g. a laptop product family, a mobile phone
series). In aspects, one or more parameters 510 may also depend
implicitly on other design factors such as cost, visual appeal,
form factor, screen real-estate, case material selection, hardware
layout, signal types, communication standards, and assembly
considerations amongst others of the consumer electronics device
10, 11, 610.
[0115] In aspects, one or more parameters 510 may be incorporated
into the audio enhancement system 500 to create an enhanced audio
capability on the associated consumer electronics device 10, 610.
Alternatively, additionally, or in combination, one or more
parameters 510 may be used to optimize the AES 500 essentially
being intimately integrated into the AES 500 architecture to
provide the enhanced audio experience from the CED 10, 610.
[0116] FIGS. 6a-b show a consumer electronics device 610 and audio
spectral response obtained therefrom. The consumer electronics
device 610 (e.g. a smartphone) may be configured to produce an
audio output signal 611. The CED 610 may include an AES 500 in
accordance with the present disclosure. The CED 610 may be tested
to determine an associated acoustic signature during the design
process, the manufacturing process, the validation process, or the
like, and the audio performance thereof adjusted through
programming of the AES included therein.
[0117] In the non-limiting example shown in FIGS. 6a-b, the target
acoustic response, in this case being a relatively flat and broad
frequency response (e.g. a wide pass band signal). In aspects, the
target acoustic response may be used to construct a time equivalent
impulse response, which may form the target acoustic response
employed in a tuning process with an optimization system 101, 201
in accordance with the present disclosure.
[0118] FIG. 6b shows a comparison between a frequency response test
of the audio output 611 of the consumer electronics device 610
including a conventional loudspeaker assembly (trace 620), with a
highly integrated loudspeaker assembly (trace 625), and with both
an integrated loudspeaker assembly and an associated and optimized
audio enhancement system in accordance with the present disclosure
(trace 630). The figure shows a log-linear frequency response plot
with frequency along the horizontal axis and amplitude of the audio
output 611 along the vertical axis, in units of decibels.
[0119] The trace 630 shows the frequency response of the consumer
electronics device 610 with an integrated loudspeaker assembly and
an audio enhancement system in accordance with the present
disclosure. As seen from the figure, when tuned to the final
desired properties of the CED 610, in this case, the audio
enhancement system (AES) 500 levels out the frequency response of
the CED 610, while further extending the bass range (e.g. lower
frequency range) of the frequency response versus either responses
shown in either of the other traces (e.g. compared to trace 620 and
trace 625).
[0120] These improvements in the audio output 611 from the consumer
electronics device 610 may be advantageous for improving user
experience, increasing audio performance from the device (e.g.
extending the dynamic range, increasing the available sound
pressure level, extending the bass response thereof, etc.),
decreasing part to part variability, improving manufacturing
yields, and for standardizing audio performance (e.g. providing a
consistent audio performance consistent with the target acoustic
response) in applications that run on the consumer electronics
device 610.
[0121] By using an optimization system 101, 201 in accordance with
the present disclosure, to analyze the frequency response, impulse
response, etc. of the consumer electronics device 610 an accurate
and compensate able calculation of an acoustic signature for the
consumer electronics device 610 may be made. Optimal compensating
parameters 510 for an associated audio enhancement system 500 can
be derived from the acoustic signature. In aspects, the acoustic
signature may be compensated for in the audio enhancement system
500 to produce an enhanced audio output 611 from the CED 610 (e.g.
so as to more closely match a target acoustic response). In
aspects, the acoustic signature may also be used to derive one or
more parameters 510 in the audio enhancement system 610 thus
providing a means for compensating for the acoustic signature of
the consumer electronics device 610.
[0122] The CED 10, 610 may include one or more audio sampling
components (e.g. microphones, speakers with dual I/O functionality,
etc.). The audio sampling component may be used as a form of
feedback for assessing the audio performance of the CED 10, 610 in
practice. In aspects, the audio enhancement system may include one
or more reconfigurable parameters 510, which may be mildly adjusted
in the field to compensate for various acoustic property changes
(e.g. due to aging, dust buildup, etc.) that may occur throughout
the lifetime of the device. Such AES adjustments may be implemented
in a relatively robust fashion by using a combination of acoustic
output from the system, audio capture from the audio sampling
components, and implementation of a correction algorithm (e.g. on
the device, in a cloud data center, as part of a virtualized and/or
cloud based optimization system 101, 201, etc.).
[0123] FIGS. 7a-b show methods for optimizing audio performance of
a consumer electronics device including an audio enhancement system
in accordance with the present disclosure for use during a design
phase and/or a manufacturing phase of the consumer electronics
device. The methods may be implemented by and/or in cooperation
with an optimization system 101, 201 and/or a tuning rig 900 both
in accordance with the present disclosure.
[0124] FIG. 7a shows a method 702 for enhancing audio performance
of a consumer electronics device including an audio enhancement
system 500 in accordance with the present disclosure. The method
702 includes determining a set of parameters 704 for a configurable
audio processing system (e.g. an audio enhancement system 500),
optimizing and/or formulating the audio processing system with the
parameters 706, and integrating the optimized audio processing
system into the consumer electronics device 708.
[0125] The parameters 510 may be determined and/or optimized by
analyzing the consumer electronics device 10, 610 in an acoustic
test chamber (e.g. an anechoic test chamber, a tuning rig 900 in
accordance with the present disclosure, etc.) including one or more
audio sensors, and running a configuration algorithm to
pre-configure and/or determine the optimal parameters 510 for the
configurable audio processing system in combination with the
analysis. The parameters 510 may be iteratively determined through
repetition of the analysis process. In aspects, the method may be
implemented along with an optimization system 101, 201 in
accordance with the present disclosure.
[0126] In aspects, a method for enhancing audio performance of a
consumer electronics device (CED) 10, 610 may include placing the
consumer electronics device 10, 610 including an audio signal
source, one or more transducers, and an audio enhancement system
(AES) into an acoustic test chamber with a plurality of audio
sensors (e.g. microphones) spatially and optionally strategically
arranged within the acoustic test chamber and/or on or within the
CED 10, 610 (e.g. a microphone on a handset CED 610). A range of
test audio signals (e.g. impulse signals, frequency sweeps, music
clips, pseudo-random data streams, etc.) may be played on the
consumer electronics device 610, monitored and recorded with the
audio sensors and/or sensors on the consumer electronics device
610. In aspects of an initial test, the audio enhancement system
500 may substantially include an uncompensated distortion function
(a null state whereby the audio enhancement system 500 is
configured so as to not substantially affect the audio signal
pathway through the CED 10, 610). The uncompensated distortion
function may act to minimally affect the acoustic signature of the
CED 10, 610 during the initial testing procedures.
[0127] The effect of the CED 10, 610 on the test audio signals may
be measured by the audio sensors. The CED 10, 610 acoustic
signature may be estimated from cross correlation and/or comparison
of the test audio signals with the corresponding measured signals
from the audio sensors. To further improve the estimation process,
the acoustic signature of one or more elements in the acoustic test
chamber may be estimated (i.e. one or more audio sensors, the
mounting apparatus of the consumer electronics device, the effect
of any test leads or cables on the consumer electronics device,
etc.) and subsequently compensated for in the above analysis. Thus
a more true representation of the acoustic signature as well as the
acoustic responses of the CED 10, 610 to the full gamut of test
audio signals may be obtained and consequently applied to the
analysis.
[0128] The audio enhancement system 500 transfer functions may then
be parametrically configured to compensate for the acoustic
signature of the CED 10, 610. In aspects, one non-limiting approach
for calculating the audio enhancement system transfer function(s)
from the acoustic signature of the CED 10, 610 may be to implement
a time domain inverse finite impulse response (FIR) filter based
upon the estimated acoustic signature of the CED 10, 610. This may
be implemented by performing one or more convolutions of the AES
500 transfer functions with the acoustic responses of the CED 10,
610 to the audio input signals. An averaging algorithm may be used
to optimize the transfer function(s) of the AES 500 from the
outputs measured across multiple sources and/or multiple test audio
signals.
[0129] In one non-limiting example, the compensation transfer
function may be calculated from a least squares (LS) time-domain
filter design approach. If c(n) is the system response to be
corrected (such as the output of an impulse response test) and a
compensating filter is denoted as h(n), then one can construct C,
the convolution matrix of c(n), as outlined in equation 1:
C = [ c ( 0 ) 0 c ( N c - 1 ) c ( 0 ) 0 c ( N c - 1 ) ] [ equation
1 ] ##EQU00001##
[0130] where N.sub.c is the length of the response c(n). C has a
number of columns equal to the length of h(n) with which the
response is being convoluted. Assuming the sequence h has length
denoted by N.sub.h then the number of rows of C is equal to
(N.sub.h+N.sub.c-1). Then, using a deterministic least squares (LS)
approach to compare against a desired response, (in a non-limiting
example, defined as the Kronecker delta function .delta.(n-m)), one
can express the LS optimal inverse filter as outlined in equation
2:
h(n)=(C.sup.TC).sup.-1C.sup.Ta.sub.m [equation 2]
[0131] where a.sub.m(n) is a column vector of zeroes with 1 in the
mth position to create the modeling delay. The compensation filter
h(n) can then be computed from equation 2 using a range of
computational methods.
[0132] As an alternative to the Kronecker delta function, more
achievable target acoustic responses may be employed in the tuning
process. In one non-limiting example, the target acoustic response
may be calculated from a relatively flat and broad frequency
response (e.g. a wide pass band signal). The target acoustic
response may be converted into a time equivalent impulse response,
which may form the target acoustic response employed in a tuning
process with an optimization system 101, 201 in accordance with the
present disclosure.
[0133] In another non-limiting example, the parametrically
configurable transfer function(s) of the AES 500 may be iteratively
determined by subsequently running test audio signals on the CED
10, 610 with the updated transfer function(s) and monitoring the
modified acoustic signature of the CED 10, 610 with the audio
sensors. A least squares optimization algorithm may be implemented
to iteratively update the transfer function(s) between test
regiments until an optimal modified acoustic signature of the CED
10, 11, 610 is obtained. Other, non-limiting examples of
optimization techniques include non-linear least squares, L2 norm,
averaged one-dependence estimators (AODE), Kalman filters, Markov
models, back propagation artificial neural networks, Bayesian
networks, basis functions, support vector machines, k-nearest
neighbors algorithms, case-based reasoning, decision trees,
Gaussian process regression, information fuzzy networks, regression
analysis, self-organizing maps, logistic regression, time series
models such as autoregression models, moving average models,
autoregressive integrated moving average models, classification and
regression trees, multivariate adaptive regression splines, and the
like. Such algorithms may be implemented in an optimization system
101, 201 in accordance with the present disclosure.
[0134] Due to the spatial nature of the acoustic signature of a CED
10, 11, 610 the optimization process may be configured so as to
minimize error between an ideal system response and the actual
system response as measured at several locations within the sound
field of the CED 10, 11, 610. The multi-channel data obtained via
the audio sensors may be analyzed using sensor fusion approaches.
In many practical applications, the usage case of the CED 10, 11,
610 may be reasonably well defined (e.g. the location of the user
with respect to the device, the placement of the device in an
environment, etc.) and thus a suitable spatial weighting scheme can
be devised in order to prioritize the audio response of the CED 10,
11, 610 in certain regions of the sound field that correspond to
the desired usage scenario. In one, non-limiting example, the
acoustic response within the forward facing visual range of a
laptop screen may be favored over the acoustic response as measured
behind the laptop screen during such tests. In this way, a more
optimal acoustic enhancement system 500 may be formulated to suit a
particular usage case for the CED 10, 11, 610.
[0135] FIG. 7b shows a non-limiting example of a method 712 for
enhancing audio in a consumer electronics device. The method 712
includes integrating a configurable audio enhancement system into a
consumer electronics device 714, testing the consumer electronics
device during the manufacturing, validation or final testing
process 716, and updating the audio enhancement system within the
consumer electronics device 718.
[0136] The consumer electronics device may be tested 716 in an
automated optimization system 101 in accordance with the present
disclosure. The optimization system 101 may run a diagnostic test
on the consumer electronics device 10, 610 and record audio output
from the device 10, 610 obtained during the diagnostic test. An
update to the audio enhancement system 500 may be generated using
data obtained from the diagnostic test, and the automated test cell
may update the audio enhancement system 500 on the consumer
electronics device 10, 610.
[0137] The method 712 may include hardcoding the optimized audio
processing system into a hardware descriptive language (HDL)
implementation. An HDL implementation may be advantageous for
simplifying integration of the audio processing and enhancement
system into existing processors and/or hardware on the consumer
electronics device. An HDL implementation may also be advantageous
for encrypting and protecting the parameters 510 in the audio
processing system (e.g. an audio enhancement system 500 in
accordance with the present disclosure).
[0138] Alternatively, additionally, or in combination, the method
1012 may include soft-coding the optimized audio processing system
and/or associated parameters 510 into a processor, flash, EEPROM,
memory location, or the like. Such a configuration may be used to
implement the AES in software, as a hardcoded routine on a DSP, a
processor, and ASIC, etc.
[0139] FIG. 8 shows a non-limiting example of a method for
integrating an audio enhancement system (AES) and an integrated
loudspeaker assembly both in accordance with the present disclosure
into a consumer electronics device. The method includes determining
one or more parameters 510 for use in the audio enhancement system
802, optimizing the audio enhancement system 804, hard coding the
audio enhancement system 806 into a hardware descriptive language
(HDL) implementation, and integrating the audio enhancement system
into a consumer electronics device 814. The method may include a
step of optimizing the power usage of the AES 808, optimizing the
footprint of the AES 810, and/or optimizing the hardcoded
implementation for a given semiconductor fabrication process
812.
[0140] The step of determining one or more parameters for use in
the audio enhancement system 802 may be first performed during the
design stage of the associated consumer electronics device (e.g. as
part of or to build an optimization system 101, 201, etc.). During
this first step, the consumer electronics device with an audio
enhancement system in accordance with the present disclosure, may
be tested and analyzed in an audio test facility. The results of
the testing may be used to construct an optimal set of parameters
510 for use with the associated AES 500 to compensate for acoustic
anomalies, and deficiencies in the CED. The AES 500 may be tuned
with the parameters 510 and the system may be iteratively tested
and corrected as part of the parameter determination process
802.
[0141] The step of optimizing the AES 804 may be performed and/or
updated during the final manufacturing and/or programming steps of
the CED. Such a step may be performed using an optimization system
101, 201 in accordance with the present disclosure.
[0142] The method may include optimizing the HDL implementation for
reduced power 808, reduced footprint 810, or for integration into a
particular semiconductor manufacturing process (e.g. 13 nm-0.5
.mu.m CMOS, CMOS-Opto, HV-CMOS, SiGe BiCMOS, etc.) 812. This may be
advantageous for providing an enhanced audio experience for a
consumer electronics device without significantly impacting power
consumption or adding significant hardware or cost to an already
constrained device.
[0143] FIG. 9 shows aspects of a tuning rig 900 for testing,
validating, programming, and/or updating an audio enhancement
system 500 within a consumer electronics device (CED) 10 in
accordance with the present disclosure. The tuning rig 900 may
include an acoustic test chamber 910 (e.g. an anechoic chamber,
semi-anechoic chamber, etc.) or alternatively a chamber with an
improved acoustic quality (e.g. reduced echo, reduced influence
from external sound sources, etc. compared to a manufacturing
environment) in which to place a CED 10, 11, 610 for testing. The
tuning rig 900 may include and/or interface with an optimization
system 101, 201 in accordance with the present disclosure to
perform the tuning and/or optimization process.
[0144] In aspects, the tuning rig 900 may include one or more
microphones 920a,b spaced within the acoustic test chamber 910 so
as to operably obtain acoustic signals emitted from the CED 10
during a testing and optimization procedure. The tuning rig 900 may
include a boom 930 for supporting the CED 10. The boom 930 may
include a connector for communicating with the CED 10 during a
testing and optimization procedure (e.g. so as to send audio data
streams to the CED 10 for testing, to program audio parameters on
the CED 10, etc.). The boom 930 may be connected to a mounting arm
940 on the wall of the acoustic test chamber 910. The mounting arm
940 may include a rotary mechanism for rotating the CED 10 about
the boom axis during a testing and optimization procedure. The
mounting arm 940 may be electrically interconnected with a
workstation 960 such as via cabling 950.
[0145] A workstation 960 is shown in the form of a computer
workstation. Alternatively or in combination, the workstation 960
may include or be configured as a customized hardware system. The
hardware configuration of the workstation 960 may include a data
collection front end, a hardware analysis block (e.g. part of an
optimization system 101, 201), and a programmer. Such a
configuration may be advantageous for rapid, autonomous
optimization of audio output from and/or audio signal processing
aspects of the CED 10, 11, 610 during manufacturing. The
workstation 960 may include at least a portion of an optimization
system 101 in accordance with the present disclosure.
[0146] In aspects, the workstation 960 may have support for user
input and/or output, for example to observe the programming
processes, to observe the differences between batch programming
results, for controlling the testing process, visualizing the
design specification, etc. Alternatively or in combination, the
workstation 960 may communicate audio test data and/or programming
results to a cloud based data center. The cloud based data center
may be configured to accept audio test data, to compare with prior
programming histories and/or the master design
record/specification, and to generate audio programming information
to be sent to the CED 10. The cloud based data center may include
an optimization system 101, 201 in accordance with the present
disclosure.
[0147] The workstation 960 and/or cloud based data center may be
configured to communicate relevant audio streaming and program data
with the CED 10 wirelessly.
[0148] In aspects, the tuning rig 900 may be provided in a retail
store or repair center to optimize audio performance of a CED 10,
610 including an audio enhancement system in accordance with the
present disclosure. In aspects, a fee for service implementation of
a tuning rig 900 may be used in a retail store in order to optimize
the audio performance of a customer's CED, perhaps after selection
of a new case for their CED, at the time of purchase, during a
service session, during repair, during first boot, or the like.
Such systems may provide the discerning consumer with the option to
upgrade the audio performance of their device and allow a retail
center to offer a unique experience enhancing service for their
consumers.
[0149] It will be appreciated that additional advantages and
modifications will readily occur to those skilled in the art.
Therefore, the disclosures presented herein and broader aspects
thereof are not limited to the specific details and representative
embodiments shown and described herein. Accordingly, many
modifications, equivalents, and improvements may be included
without departing from the spirit or scope of the general inventive
concept as defined by the appended claims and their
equivalents.
* * * * *