U.S. patent application number 16/195224 was filed with the patent office on 2019-03-21 for dynamic sound adjustment.
This patent application is currently assigned to Bose Corporation. The applicant listed for this patent is Bose Corporation. Invention is credited to Shiufun Cheung, Michael S. Dublin, Zukui Song.
Application Number | 20190090074 16/195224 |
Document ID | / |
Family ID | 53002802 |
Filed Date | 2019-03-21 |
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United States Patent
Application |
20190090074 |
Kind Code |
A1 |
Song; Zukui ; et
al. |
March 21, 2019 |
Dynamic Sound Adjustment
Abstract
Among other things, one or more non-transitory machine-readable
media storing instructions are described. The stored instructions
are executable by one or more processing devices to perform
operations comprising analyzing an effect of noise in a spatial
unit on sound in the spatial unit, at least part of the sound being
produced by audio signals; selecting an adjustment curve among a
group of adjustment curves based on one or more characteristics of
the noise; and determining an amount of adjustment to be made to
the audio signals based on the analyzed effect and the selected
adjustment curve.
Inventors: |
Song; Zukui; (Wellesley,
MA) ; Cheung; Shiufun; (Lexington, MA) ;
Dublin; Michael S.; (Arlington, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bose Corporation |
Framingham |
MA |
US |
|
|
Assignee: |
Bose Corporation
Framingham
MA
|
Family ID: |
53002802 |
Appl. No.: |
16/195224 |
Filed: |
November 19, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15433459 |
Feb 15, 2017 |
10142749 |
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16195224 |
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14224745 |
Mar 25, 2014 |
9615185 |
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15433459 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04R 29/00 20130101;
H03G 5/165 20130101; H04R 2499/13 20130101; H04R 2430/01 20130101;
H03G 3/32 20130101 |
International
Class: |
H04R 29/00 20060101
H04R029/00; H03G 5/16 20060101 H03G005/16; H03G 3/32 20060101
H03G003/32 |
Claims
1. One or more non-transitory machine-readable media storing
instructions that are executable by one or more processing devices
to perform operations comprising: obtaining a first signal that is
present in a cabin of a vehicle comprising a first unwanted noise
signal and a second desired audio signal that is produced by a
vehicle audio system of the vehicle provided with an input signal;
adaptively filtering the first obtained signal to form an estimate
of the first unwanted noise signal; comparing a first measurement
in a first frequency band of the estimated first unwanted noise
signal with a second measurement in a second frequency band of the
estimated first unwanted noise signal, the second frequency band
being different from the first frequency band; determining a first
operating condition of the vehicle; selecting, based on both the
comparison and the determined first operating condition, a first
gain to be applied to the vehicle audio system input signal to
compensate for noise interference, and; applying the first gain to
one or more first signal components of the vehicle audio system
input signal.
2. The one or more non-transitory machine-readable media of claim
1, wherein comparing the first noise measurement with the second
noise measurement comprises calculating a ratio of the first noise
measurement in the first frequency band and the second noise
measurement in the second frequency band.
3. The one or more non-transitory machine-readable media of claim
1, further comprising: determining a second gain value based on the
first comparison; and applying the second gain value to one or more
second signal components of the vehicle audio system input
signal.
4. The one or more non-transitory machine-readable media of claim
3, wherein the one or more first signal components are in a low
frequency band, and the one or more second signal components are in
a high frequency band.
5. The one or more non-transitory machine-readable media of claim
3, wherein determining the second gain value comprises: based on
the comparison of the first and second measurements of the
estimated first unwanted noise signal and the determined first
operating condition of the vehicle; calculating a signal to noise
ratio (SNR); and determining the second gain value based on the
calculated SNR and the determined first operating condition.
6. The one or more non-transitory machine-readable media of claim
3, further comprising: determining a third gain value based on the
first comparison; and applying the third gain value to one or more
third signal components of the vehicle audio system input
signal.
7. The one or more non-transitory machine-readable media of claim
6, wherein the one or more first signal components are in a low
frequency band, the one or more second signal components are in a
high frequency band, and wherein the one or more third signal
components are in a mid-frequency band, at least some frequencies
of the one or more third signal components being lower than
frequencies of the high frequency band and higher than frequencies
of the low frequency band.
8. The one or more non-transitory machine-readable media of claim
6, wherein determining the third gain value comprises: based on the
comparison of the first and second measurements of the estimated
first unwanted noise signal and the determined first vehicle
operating condition; calculating a signal to noise ratio (SNR); and
determining the third gain value based on the calculated SNR and
the determined first vehicle operating condition.
9. The one or more non-transitory machine-readable media of claim 1
further comprising: a second comparison comparing a third
measurement in the first frequency band of the estimated first
unwanted noise signal with a fourth measurement in the second
frequency band of the estimated first unwanted noise signal;
determining a second operating condition of the vehicle; selecting,
based on both the determined second operating condition and the
second comparison a fourth gain to be applied to the vehicle audio
system input signal to compensate for noise interference, wherein
the fourth gain may differ from the first gain, even if results of
the first and second comparisons are the same.
10. The one or more non-transitory machine-readable media of claim
9, wherein comparing the third noise measurement with the fourth
noise measurement comprises calculating a ratio of the third noise
measurement in the first frequency band and the fourth noise
measurement in the second frequency band.
11. The one or more non-transitory machine-readable media of claim
1, wherein the first measurement of the estimated first unwanted
noise signal comprises first noise energy in the first frequency
band, and the second measurement of the estimated first unwanted
noise signal comprises second noise energy in the second frequency
band.
12. A controller comprising: a processor; and a storage device that
stores a program for execution by the processor, the program
comprising instructions configured to cause the processor to
perform operations comprising: comparing a first measurement of an
estimated first unwanted noise signal in a first frequency band
with a second measurement of the estimated first unwanted noise
signal in a second frequency band, the second frequency band being
different from the first frequency band; selecting, based on the
comparison, a predetermined adjustment map, wherein the
predetermined adjustment map relates a gain to be applied to an
input signal of a vehicle audio system of a vehicle to compensate
for noise interference to a first signal to noise ratio, wherein
the first signal to noise ratio relates an audio signal produced by
the vehicle audio system to the estimated first unwanted noise
signal, wherein the adjustment map was predetermined for a first
set of operating conditions of the vehicle; determining a first
gain value based on the signal to noise ratio and the selected map;
and applying the first gain value to one or more first signal
components of the input signal of the vehicle audio system of the
vehicle.
13. The controller of claim 12, wherein the operations of comparing
the first measurement the estimated first unwanted noise signal
with the second measurement the estimated first unwanted noise
signal comprise operations of calculating a ratio of the first
measurement in the first frequency band and the second measurement
in the second frequency band.
14. The controller of claim 12, wherein the operations of
identifying the first adjustment map comprise interpolating the
first adjustment map from a first predefined set of adjustment
maps.
15. The controller of claim 12, further comprising instructions
configured to cause the processor to perform operations comprising:
determining a second gain value based on the comparison; and
applying the second gain value to one or more second signal
components of the input signal of the vehicle audio system of the
vehicle.
16. The controller of claim 15, wherein the one or more first
signal components are in a low frequency band, and the one or more
second signal components are in a high frequency band.
17. The controller of claim 15, wherein operations of determining
the second gain value comprise: identifying a second adjustment map
based on the comparison of the first and second measurements of the
estimated first unwanted noise signal; calculating a signal to
noise ratio (SNR); and determining the second gain value based on
the calculated SNR and the second adjustment map.
18. The controller of claim 15, further comprising instructions
configured to cause the processor to perform operations comprising:
determining a third gain value based on the comparison; and
applying the third gain value to one or more third signal
components of the input signal of the vehicle audio system of the
vehicle.
19. The controller of claim 18, wherein the one or more first
signal components are in a low frequency band, the one or more
second signal components are in a high frequency band, and wherein
the one or more third signal components are in a mid-frequency
band, at least some frequencies of the one or more third signal
components being lower than frequencies of the high frequency band
and higher than frequencies of the low frequency band.
20. The controller of claim 18, wherein operations of determining
the third gain value comprise: identifying a third adjustment map
based on the comparison of the first and second measurements of the
estimated first unwanted noise signal; calculating a signal to
noise ratio (SNR); and determining the third gain value based on
the calculated SNR and the third adjustment map.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/433,459 filed Feb. 15, 2017, which is a divisional of U.S.
application Ser. No. 14/224,745, filed Mar. 25, 2014, both of which
are incorporated herein by reference in their entirety.
TECHNICAL FIELD
[0002] This disclosure generally relates to dynamic sound
adjustment, e.g., to overcome the effect of noise on sound
reproduction in a moving vehicle.
BACKGROUND
[0003] The reproduction of music or speech in a moving vehicle may
be degraded by variable acoustic noise present in the vehicle. This
noise may result from, and be dependent upon, vehicle speed, road
condition, weather, and condition of the vehicle. The presence of
increased noise may hide soft sounds of interest and lessen the
fidelity of music or the intelligibility of speech. A driver and/or
passenger(s) of the vehicle may partially compensate for the
increased noise by increasing the volume of the audio system.
However, when the vehicle speed decreases or the noise goes away,
the increased volume of the audio system may become too high,
requiring the driver or the passenger(s) to decrease the volume. A
frequent need to increase and decrease the volume is an
inconvenience and can also be a safety hazard because it can
distract the driver's attention.
SUMMARY
[0004] In one aspect, the disclosure features one or more
non-transitory machine-readable media storing instructions that are
executable by one or more processing devices to perform operations
comprising: analyzing an effect of noise in a spatial unit on sound
in the spatial unit, at least part of the sound being produced by
audio signals; selecting an adjustment curve among a group of
adjustment curves based on one or more characteristics of the
noise; and determining an amount of adjustment to be made to the
audio signals based on the analyzed effect and the selected
adjustment curve.
[0005] In another aspect, the disclosure features a controller
comprising a processor and a storage device that stores a program
for execution by the processor. The program comprises instructions
configured to cause the processor to perform operations comprising:
analyzing an effect of noise in a spatial unit on sound in the
spatial unit, at least part of the sound being produced by audio
signals; selecting an adjustment curve among a group of adjustment
curves based on one or more characteristics of the noise; and
determining an amount of adjustment to be made to the audio signals
based on the analyzed effect and the selected adjustment curve.
[0006] In another aspect, the disclosure features a system
comprising an acoustic system for producing audio signals, a
detector for detecting sound, and a controller. At least part of
the sound is produced by the audio signals and the sound comprising
noise. The controller is configured to perform operations
comprising: analyzing an effect of noise in a spatial unit on sound
in the spatial unit; selecting an adjustment curve among a group of
adjustment curves based on one or more characteristics of the
noise; and determining an amount of adjustment to be made to the
audio signals based on the analyzed effect and the selected
adjustment curve.
[0007] In another aspect, the disclosure features one or more
non-transitory machine-readable media storing instructions that are
executable by one or more processing devices to perform operations
comprising: comparing a first noise measurement in a first
frequency band with a second noise measurement in a second
frequency band different from the first frequency band; determining
a first gain value based on the comparison of the first and second
noise measurements; and applying the first gain value to one or
more first signal components of an audio signal.
[0008] Embodiments of the one or more non-transitory
machine-readable media, controllers, and systems may include one or
more of the following features. Analyzing the effect of noise on
the sound comprises calculating a signal to noise ratio. The
operations comprise determining the characteristics of the noise.
The characteristics of the noise comprise a first level of noise
energy in a first frequency band and a second level of noise energy
in a second frequency band. Different adjustment curves correspond
to different ratios of the first level to the second level. The
spatial unit comprises a cabin of a moving vehicle and the noise
comprises wind noise. The audio signals span a spectral frequency
and selecting an adjustment curve comprises selecting a first
adjustment curve for a first band of the frequency range among a
first group of adjustment curves for the first band and selecting a
second adjustment curve for a second band of the frequency range
among a second group of adjustment curves for the second band.
Determining an adjustment comprises determining a first adjustment
for the audio signals in the first band and a second adjustment for
the audio signals in the second band, the first and second
adjustments being determined independently. The analyzing, the
selecting, and the determining are performed in real time.
Selecting an adjustment curve comprises selecting a third
adjustment curve for a third band of the frequency range among a
third group of adjustment curves determined for the third band, and
the first, second and third bands form the entire spectral
frequency range. The operations comprise performing the adjustment
to the audio signals. Comparing the first noise measurement with
the second noise measurement comprises calculating a ratio of the
first noise measurement in the first frequency band and the second
noise measurement in the second frequency band. Determining the
first gain value comprises: identifying a first adjustment curve
based on the comparison of the first and second noise measurements;
calculating a signal to noise ratio (SNR); and determining the
first gain value based on the calculated SNR and the first
adjustment curve. Identifying the first adjustment curve comprises
interpolating the first adjustment curve from a first predefined
set of adjustment curves. A second gain value is determined based
on the comparison; and the second gain value is applied to one or
more second signal components of the audio signal. The one or more
first signal components are in a low frequency band, and the one or
more second signal components are in a high frequency band.
Determining the second gain value comprises: identifying a second
adjustment curve based on the comparison of the first and second
noise measurements; calculating a signal to noise ratio (SNR); and
determining the second gain value based on the calculated SNR and
the second adjustment curve. A third gain value is determined based
on the comparison; and the third gain value is applied to one or
more third signal components of the audio signal. The one or more
third signal components are in a third mid-frequency band. At least
some frequencies of the one or more third signal components is
lower than frequencies of the high frequency band and higher than
frequencies of the low frequency band. Determining the third gain
value comprises: identifying a third adjustment curve based on the
comparison of the first and second noise measurements; calculating
a signal to noise ratio (SNR); and determining the third gain value
based on the calculated SNR and the third adjustment curve. The
first noise measurement comprises first noise energy in the first
frequency band, and the second noise measurement comprises second
noise energy in the second frequency band.
[0009] Two or more of the features described in this disclosure,
including those described in this summary section, may be combined
to form implementations not specifically described herein.
[0010] The details of one or more implementations are set forth in
the accompanying drawings and the description below. Other
features, objects, and advantages will be apparent from the
description and drawings, and from the claims.
DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 is a block diagram showing an example audio control
system.
[0012] FIG. 2 is a flow diagram showing an example process
performed by an audio control system in making an audio
adjustment.
[0013] FIG. 3 is a block diagram showing example interactions among
different parts of an audio control system in making an audio
adjustment.
[0014] FIGS. 4A and 4B are plots of noise characteristics curves
under different conditions.
[0015] FIGS. 5A-5C are plots of example adjustment curves.
[0016] FIGS. 6A and 6B are plots of example double-band adjustment
schemes.
[0017] FIG. 6C is an example plot of a three-band adjustment
scheme.
DETAILED DESCRIPTION
Overview
[0018] FIG. 1 shows a block diagram of an example audio control
system 100 installed in a vehicle (not shown). The audio control
system 100 is configured to mitigate effects of variable noise on
the listening experience by adjusting, automatically and
dynamically, the music or speech signals played by an acoustic
system 106 in a moving vehicle. The system 100 therefore promotes a
consistent listening experience without typically requiring
significant manual intervention. In this example, the audio control
system 100 includes one or more controllers 104 in communication
with one or more noise detectors 102 and an acoustic system 106. An
example of a noise detector includes a microphone placed in a cabin
of the vehicle. The microphone is typically placed at a location
near a user's ears, e.g., along a headliner of the passenger cabin.
Other examples of noise detectors include speedometers and
electronic data pertaining to engine revolutions per minute, which
can provide information that is indicative of the level of noise
perceived in the passenger cabin. An example of a controller
includes, but is not limited to, a processor, e.g., a
microprocessor. The acoustic system 106 outputs sound into the
vehicle based on the input signals of music or speech.
[0019] Mitigation of the effect of noise on the perceived sound can
be achieved by adjusting the audio signals used for playback by the
acoustic system 106 in two or more spectral bands. The adjustment
may be done automatically, i.e. without manual intervention. The
controller(s) 104 are programmed to analyze continuously the noise
detected by the detector(s) 102 and the sound produced by the
acoustic system 106. The controller(s) 104 may be programmed to
interact with the acoustic system 106 to adjust the audio signals
based on the analysis, e.g., to modify the gain. The signals can be
adjusted in two or more spectral bands independently of each other.
The analysis and adjustment can be performed using computer
programs executed by the controller(s). In some cases, different
computer program processes or different parts of processes are
encoded in different program modules.
[0020] FIG. 2 is a flow diagram illustrating an example process 200
in which the audio control system 100 of FIG. 1 automatically
adjusts audio signals in real time in response to the noise changes
in a vehicle. According to process 200, upon receiving detected
noise from detector(s) 102, the controller(s) analyzes (202) the
effect of the noise on the sound perceived within the vehicle,
which is the output of the acoustic system 106. Based on the
analysis (202), the controller(s) chooses (204) an adjustment curve
from a group, or family, of adjustment curves, which is also
referred to as the adjustment map. The adjustment map may be stored
in memory and accessible to the controller(s) or stored in internal
controller memory. As described in more detail below, the
adjustment map can be predetermined and the chosen adjustment curve
is a curve in the family of curves that best addresses (e.g.,
corrects for, compensates for, or the like) the deleterious effect
of the noise. The controller(s) determines (206) an amount of
adjustment to be made to each of a predetermined number of spectral
bands of the current signals. The determination can be made based
on the noise effect and the selected adjustment curve for a
specific band. The adjustment may be performed (208) in each
spectral band independently of the other spectral band(s).
[0021] FIG. 3 is a block diagram showing example interactions that
occur among different parts of the audio control system 100 during
the example process 200 of FIG. 2. As shown in FIG. 3, noise
detector 102 delivers data corresponding to the detected noise
level in the vehicle to the controller(s) 104. A module 302, which
may be executed by the controller(s), receives the data and
separates the noise, e.g., using adaptive filtration, from the
current output audio 308. In some implementations, module 302 is
configured to estimate separately low- and high-frequency noise
levels to allow the audio control system to adjust the signals to
compensate for effects from noise with different spectral shapes.
Typically, the low-frequency noise can originate from, or be
dependent on, vehicle speed, road conditions, engines conditions,
etc. Typically, the high-frequency noise can originate from wind,
rain, or the vehicle passing over bridges or through tunnels. A
source-analysis module 304, which can be a lossy-peak level
detector, executed by the controller(s) analyzes the sound from
input audio 306, which is produced by the acoustic system 106. The
controller(s) use the information from the noise-detection module
302 to select adjustment curves from an adjustment map on a storage
device 312. Another module 314, also executed by the controller(s),
then combines the information from the source-analysis module 304
and the noise-detection module 302 to calculate SNR
(signal-to-noise ratio) values. The SNR values are used to
determine adjustment values from the selected adjustment curves.
Based on the derived adjustment values, the controller performs
multi-band, e.g., dual-band or three-band, spectral adjustment 310
to the input audio 306.
Adjustment Maps
1. Construction
[0022] FIG. 4A contains a plot 400 of noise characteristics curves
404, 406, 408 showing the spectral shape of noise inside a vehicle
moving at three different speeds, which have been measured with the
vehicle's windows open. FIG. 4B contains a plot 402 of noise
characteristics curves 410, 412, 414 inside the same moving vehicle
at the same three different speeds, which have been measured with
the vehicle's windows closed. The curves 404, 406, 408 have a
generally common spectral shape that can be roughly represented by
a linear curve 420 that extends from the low frequencies to the
high frequencies. The curves 410, 412, 414 have another generally
common spectral shape that can be roughly represented by a linear
curve 422 that extends from the low frequencies to the high
frequencies. In this example, all curves show that the major noise
contribution is from the low-frequency band 428, 430, which can
reach an upper cutoff frequency of up to 200 Hz. The similarities
in the shape of each curve indicate that the shape of a noise
spectrum is not significantly affected, in this example, by varying
conditions such as vehicle speed. When noise in the low-frequencies
rises with the vehicle speed, noise in the high-frequencies appears
to rise proportionately.
[0023] However, the curves of the two different plots show
dissimilarities around the high-frequency band 424, 426. A typical
high-frequency band can be characterized by a lower cutoff
frequency, which can be as low as 2 kHz, and an optional upper
cutoff frequency above that. The dissimilarities in the
high-frequency band may be caused by wind-noise differences
experienced inside the vehicle when the windows are in different
states (e.g., open or closed). In some implementations, the
dissimilarities in the high-frequency band can also be caused by
other condition changes. As the noise in the high-frequency band
changes with different conditions, the linear curves 420, 422,
which approximate the spectral shape of the noise, also change. A
ratio of the magnitude of the noise energy at the low-frequency
band 428 to the magnitude of the noise energy at the high-frequency
band 424 is different from that of the magnitude of the noise
energy in the low-frequency band 430 to the magnitude of the noise
energy in the high-frequency band 426.
[0024] In some implementations, the adjustment map includes
multiple adjustment curves each corresponding to a different noise
spectrum represented by the ratio between the magnitudes of the
noise energy in the low frequency band and the noise energy in the
high-frequency band. Because of differences in the noise spectrum,
the adjustment maps can be vehicle specific.
[0025] In some implementations, the adjustment curves in the map
are based on the SNR estimated by the controller. In this instance,
the SNR is the ratio between the estimated signal level, which is
derived from the source-analysis module 304 and an estimated noise
level, which is typically the magnitude of the noise energy
associated with the low-frequency band for automobile vehicles.
FIG. 5A shows a graph 500 that contains an example adjustment curve
506. The adjustment curve 506 is a mapping between the SNR depicted
in decibels (dB) on the horizontal axis, and the adjustment value,
e.g., gain, depicted also in decibels (dB) on the vertical axis.
The curve 506 includes multiple linear regions. In this example, in
a region 502 between an onset threshold SNR, SNR.sub.T, and a
specified SNR, SNR.sub.1, the amount of adjustment linearly
increases from zero dB to a maximum adjustment value of A dB; in a
region 504 below SNR.sub.1, a constant adjustment having the
maximum value of A dB is applied; and in a region 508, above
SNR.sub.T, no adjustment, or an adjustment value of zero dB is
applied. In some implementations, an adjustment curve may include
more than two linear regions, each having a different slope.
[0026] In some implementations, an adjustment curve can be
mathematically specified using one or more of the following
parameters: the maximum adjustment value A, the number of linear
regions, the slope of each linear region, the onset threshold
SNR.sub.T, and, if necessary, for each subsequent linear region,
the starting SNR value for each region. In the example shown in
FIG. 5A, the curve 506 can be specified once SNR.sub.T, A, and the
slope of the curve in the region 502 are determined.
[0027] In some implementations, in a map for a given vehicle,
different curves can be selected based on different noise
characteristics associated with different conditions of the given
vehicle. For example, for the curve 506 shown in plot 500 of FIG.
5A and plot 520 of FIG. 5B, an onset threshold SNR.sub.T and a
slope of the curve in the region 502 can be selected based on the
ratio between the noise energy in the high-frequency and the
low-frequency bands discussed with respect to FIGS. 4A and 4B. The
plot 520 also includes another curve 510, as shown in FIG. 5B,
having an onset threshold SNR.sub.T' and the slope between the
onset threshold SNR.sub.T' and a mid-level SNR, SNR.sub.1', that is
different from the slope of the curve 506 in the region 502. The
plot 520 of FIG. 5B can be considered as an adjustment map or part
of an adjustment map.
[0028] Referring to FIG. 5C, an adjustment map can include
additional adjustment curves as compared to the plot 500 of FIG. 5A
or the plot 520 of FIG. 5B, some of which, but not all, are shown
in a plot 540. Each of these curves may be selected based on the
aforementioned ratio between the noise energy levels of the
low-frequency and high-frequency bands of the noise spectrum as
depicted in FIGS. 4A and 4B. The group of adjustment curves in the
map corresponds to a range of ratios caused by different conditions
that lead to different relative levels of noise in the low- and
high-frequency bands.
[0029] An adjustment map can be determined and programmed into an
audio control system of a vehicle. The determination can be made
empirically. In some implementations, two adjustment curves 542,
544 are respectively determined under two boundary conditions that
correspond to an upper boundary of the range of ratios and a lower
boundary of the range of ratios. All other curves can be created by
linear interpolation between the two curves 542, 544. For example,
a user can test drive a vehicle with all windows closed and with
music or speech playing within the vehicle. Test equipment, e.g.,
including a built-in noise detector, such as the noise detector 102
of FIG. 1, and controller(s), such as the controller(s) 104 of FIG.
1, can be used to receive and analyze the effect of the noise on
the sound. For example, SNRs at different times can be calculated.
The user can test drive the vehicle under different road conditions
and at different speeds. Once the user notices that a sound
adjustment is required in order to obtain a consistent audio
experience (as compared to when the vehicle is stationary or when
the vehicle is under other conditions), the user can manually
record or trigger the controller(s) to record the SNR at the moment
as the onset SNR. The user can manually adjust the audio signals to
reach a consistent audio experience at different times. The
adjusted value can be recorded by the controller(s) in association
with the SNRs at those different times. The slope of the adjustment
curve, e.g., the slope of the curve 542 between SNR.sub.T' and
SNR.sub.1, can therefore be determined. Furthermore, a maximum
adjustment amount A can be determined, e.g., when the vehicle is
moving at maximum speed. The determined parameters for one curve
can be stored in, or otherwise made accessible to, the
controller(s).
[0030] Similarly, the user can test drive a vehicle with all
windows open and with music or speech playing within the vehicle to
construct another curve, e.g., the curve 544 of FIG. 5C. One or
more additional curves can be built, e.g., with one window open and
all other windows closed or under other conditions. As described
previously, the one or more additional curves may also be
constructed between the two curves 542, 544 using mathematical
interpolation.
[0031] In addition, for each vehicle, an adjustment map having
multiple adjustment curves can be constructed for each of a
predetermined number of spectral bands. For example, using the
empirical construction process described above, instead of
adjusting the signal uniformly across the entire sound spectrum,
the user adjusts the signals in different bands independently to
maintain the desired consistency. For each SNR, the adjustments in
the multiple bands are recorded to construct different adjustment
maps for the different bands.
2. Implementations
[0032] As described above with respect to FIG. 2, one of multiple
curves of a map determined for a vehicle may be selected in real
time for use in real time sound adjustment. As used herein, "in
real time" means "simultaneous with the music or speech being
played in a moving vehicle." The selection can be based on the
detected real time noise characteristics, e.g., the magnitude of
the noise level in the low- and high-frequency bands and their
ratio. For example, when the vehicle has the adjustment map
partially shown in FIG. 5B and is driven with all windows open, the
audio control system 100 (FIG. 1) of the vehicle determines the
noise characteristics at that moment and chooses curve 510. The
selection of the curve 510 affects the adjustment even when the
real time SNR is found to be the same under the different
conditions corresponding to the curves 506. For example, for the
same real time SNR.sub.3, an adjustment of A.sub.2 is to be made to
the audio signals, which is higher than an adjustment of A.sub.1
had the curve 506 been chosen.
[0033] In reality, the real time SNR under the different conditions
corresponding to the curves 506, 510 may be different. For example,
if SNR.sub.3 corresponds to the condition in which all windows of
the vehicle are closed, then the actual SNR corresponding to the
condition in which all windows of the vehicle are open is likely to
be SNR.sub.4, which is smaller than SNR.sub.3. Using the curve 506,
an actual adjustment of A.sub.3 larger than both A.sub.1 and
A.sub.2 is to be made to the audio signals.
Multi-Band Adjustment
[0034] Referring again to FIGS. 4A and 4B, the noise
characteristics curves show that the noise energy is concentrated
in the low frequencies. Differences in the spectral shape of the
noise may lead to different effects on the sound. Different
spectral components in the audio signal may also react differently
to the noise. To maintain a tonal balance of the sound, the audio
signals can be adjusted independently in different bands of its
spectrum using different adjustment maps. Sound with appropriate
tonal balance can be perceived as more natural by a user than that
lacking an appropriate tonal balance.
[0035] FIG. 6A shows an example of a two-band adjustment scheme 600
for a vehicle at a given time. The entire spectrum of the input
audio signals is separated into a band 620 that includes primarily
the low frequencies, e.g., with a center peak at roughly 45 Hz and
a band 622 that includes the rest of the frequencies in the
spectrum and that extends to the maximum frequency represented by
the specific digital representation of the input audio signal, also
commonly known as the Nyquist frequency. The adjustment scheme 600
includes a wide-band boost 602 to be applied to the entire spectrum
including both bands 620, 622. The adjustment scheme 600 also
includes a bass boost 604 in the band 620, where the total
adjustment is the sum of the wide-band boost 602 and the bass boost
604. The magnitudes of the wide-band boost and the bass boost are
calculated in real time based on the adjustment values derived from
the adjustment curves selected from the adjustment maps described
previously. The spectral shapes of the wide-band boost 602 and the
bass boost 604 can be pre-determined by the choice of filters used
to effect the boosts. In this example, the wide-band boost 602 is
implemented using a constant gain across the entire spectrum, while
the bass boost 604 is implemented using a low-order band-pass
filter.
[0036] FIG. 6B shows an alternative scheme 640 to the scheme 600
that achieves the same adjustment for the same vehicle at the same
given time. In the scheme 640, the adjustments in the two different
bands 620, 640 are implemented independently with minimal spectral
overlap. A typical lower-frequency band 620 can have DC (direct
current, or zero frequency) as its lower bound and extend to an
upper cutoff frequency, e.g., of up to 200 Hz. A typical
higher-frequency band 640 can extend from the upper cutoff
frequency of the lower-frequency band to the edge of the spectrum,
or the Nyquist frequency. In other words, there is no boost that is
applied to the entire spectrum. Instead, a boost 642 is applied to
the band 622. The boost 642 has the same magnitude as the wide-band
boost 602 in the band 622. A boost 644 is applied to the band 620.
The boost 644 corresponds to the sum of the bass boost 604 and the
wide-band boost 602 of FIG. 6A.
[0037] In some implementations, the audio signals are adjusted in
three separate frequency bands of an entire sound spectrum. The
three bands can include a low frequency band, e.g., the band 620 of
FIGS. 6A and 6B, a mid-frequency band, and a treble band. For
example, the band 622 of FIGS. 6A and 6B can be split into the
mid-frequency band and the treble band. In that example scenario,
the low-frequency band extends from DC (zero frequency) to a cutoff
frequency, e.g., of up to 200 Hz. The mid-frequency band extends
from the cutoff frequency of the low-frequency band to a higher
cutoff frequency, which can be between 3 to 6 kHz. The treble in
turn extends from the upper cutoff frequency of the mid-frequency
band and covers the remainder of the frequency range. Compared to a
single band or two bands, the three-band adjustment can increase
the flexibility in formulating adjustment curves independently for
the mid-range and treble frequencies and can improve the tonal
balance in the adjusted audio signals.
[0038] FIG. 6C shows an example of a three band adjustment scheme
650 for a vehicle at a given time. Adjustment to the audio signals
can be made independently in three bands, including, e.g., a
low-frequency band 660, a mid-frequency band 662, and a treble band
664. In this example, a bass boost 670, similar to the bass boost
644 of FIG. 6B, is applied in the band 660, a wide-band boost 674,
similar to the wide-band boost 642, is applied in the band 664, and
an adjustment 672 is applied to the band 662. Each of the boost
670, 672, 672 can be determined based on information about the
current sound, the noise, and a chosen adjustment curve from a
stored map for the vehicle. Compared to the scheme 640 of FIG. 6B
or the scheme 600 of FIG. 6A, the scheme 650 allows the adjustment
in the mid-frequency band 662 to be fine-tuned independently of the
adjustments in the other two bands 660, 664.
[0039] In some implementations, the three-band adjustment scheme
can provide a perceptual improvement to specific audio content,
e.g., to speech. Energy in speech signals are typically
concentrated in the mid-frequency region and the flexibility from
having a dedicated adjustment map to the mid-frequencies can allow
for more accurate adjustment in the presence of noise. In some
implementations, an acoustic system of a vehicle provides surround
sound presentations, with independent audio content coming from
rear speakers. When noise rises in the rear of the vehicle, e.g.,
when a rear-mounted engine is engaged, the content from the rear
speakers may be swamped, jeopardizing the carefully-tuned surround
presentation. In this scenario, the flexibility from having a
dedicated adjustment map to the mid-frequencies can also allow the
surround presentation to be preserved.
[0040] In some implementations, the sound spectrum can be separated
into four or more bands. An adjustment map can be determined for
each band, and the portion of audio signal in each band can be
independently adjusted in real time.
[0041] Embodiments of the subject matter and the functional
operations described in this specification can be implemented in
digital electronic circuitry, in tangibly-embodied computer
software or firmware, in computer hardware, including the
structures disclosed in this specification and their structural
equivalents, or in combinations of one or more of them. Embodiments
of the subject matter described in this specification can be
implemented as one or more computer programs, i.e., one or more
modules of computer program instructions encoded on a tangible non
transitory storage medium for execution by, or to control the
operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal, that is generated
to encode information for transmission to suitable receiver
apparatus for execution by a data processing apparatus. The
computer storage medium can be a machine-readable storage device, a
machine-readable storage substrate, a random or serial access
memory device, or a combination of one or more of them.
[0042] The term "data processing apparatus" refers to data
processing hardware and encompasses all kinds of apparatus,
devices, and machines for processing data, including by way of
example a programmable digital processor, a digital computer, or
multiple digital processors or computers. The apparatus can also be
or further include special purpose logic circuitry, e.g., an FPGA
(field programmable gate array) or an ASIC (application specific
integrated circuit). The apparatus can optionally include, in
addition to hardware, code that creates an execution environment
for computer programs, e.g., code that constitutes processor
firmware, a protocol stack, a database management system, an
operating system, or a combination of one or more of them.
[0043] A computer program, which may also be referred to or
described as a program, software, a software application, a module,
a software module, a script, or code, can be written in any form of
programming language, including compiled or interpreted languages,
or declarative or procedural languages, and it can be deployed in
any form, including as a stand alone program or as a module,
component, subroutine, or other unit suitable for use in a
computing environment. A computer program may, but need not,
correspond to a file in a file system. A program can be stored in a
portion of a file that holds other programs or data, e.g., one or
more scripts stored in a markup language document, in a single file
dedicated to the program in question, or in multiple coordinated
files, e.g., files that store one or more modules, sub programs, or
portions of code. A computer program can be deployed to be executed
on one computer or on multiple computers that are located at one
site or distributed across multiple sites and interconnected by a
data communication network.
[0044] The processes and logic flows described in this
specification can be performed by one or more programmable
computers executing one or more computer programs to perform
functions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC (application
specific integrated circuit). For a system of one or more computers
to be "configured to" perform particular operations or actions
means that the system has installed on it software, firmware,
hardware, or a combination of them that in operation cause the
system to perform the operations or actions. For one or more
computer programs to be configured to perform particular operations
or actions means that the one or more programs include instructions
that, when executed by data processing apparatus, cause the
apparatus to perform the operations or actions.
[0045] Computers suitable for the execution of a computer program
include, by way of example, can be based on general or special
purpose microprocessors or both, or any other kind of central
processing unit. Generally, a central processing unit will receive
instructions and data from a read only memory or a random access
memory or both. The essential elements of a computer are a central
processing unit for performing or executing instructions and one or
more memory devices for storing instructions and data. Generally, a
computer will also include, or be operatively coupled to receive
data from or transfer data to, or both, one or more mass storage
devices for storing data, e.g., magnetic, magneto optical disks, or
optical disks. However, a computer need not have such devices.
Moreover, a computer can be embedded in another device, e.g., a
mobile telephone, a personal digital assistant (PDA), a mobile
audio or video player, a game console, a Global Positioning System
(GPS) receiver, or a portable storage device, e.g., a universal
serial bus (USB) flash drive, to name just a few.
[0046] Computer readable media suitable for storing computer
program instructions and data include all forms of non volatile
memory, media and memory devices, including by way of example
semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory
devices; magnetic disks, e.g., internal hard disks or removable
disks; magneto optical disks; and CD ROM and DVD-ROM disks. The
processor and the memory can be supplemented by, or incorporated
in, special purpose logic circuitry.
[0047] Control of the various systems described in this
specification, or portions of them, can be implemented in a
computer program product that includes instructions that are stored
on one or more non-transitory machine-readable storage media, and
that are executable on one or more processing devices. The systems
described in this specification, or portions of them, can be
implemented as an apparatus, method, or electronic system that may
include one or more processing devices and memory to store
executable instructions to perform the operations described in this
specification.
[0048] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any claims or on the scope of what may
be claimed, but rather as descriptions of features that may be
specific to particular embodiments of particular inventions.
Certain features that are described in this specification in the
context of separate embodiments can also be implemented in
combination in a single embodiment. Conversely, various features
that are described in the context of a single embodiment can also
be implemented in multiple embodiments separately or in any
suitable subcombination. Moreover, although features may be
described above as acting in certain combinations and even
initially claimed as such, one or more features from a claimed
combination can in some cases be excised from the combination, and
the claimed combination may be directed to a subcombination or
variation of a subcombination.
[0049] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system modules and components in the
embodiments described above should not be understood as requiring
such separation in all embodiments, and it should be understood
that the described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0050] Particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. For example, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
As one example, the processes depicted in the accompanying figures
do not necessarily require the particular order shown, or
sequential order, to achieve desirable results. In some cases,
multitasking and parallel processing may be advantageous.
* * * * *