CN113345401A - Calibration method and device of active noise reduction system of wearable device, storage medium and terminal - Google Patents
Calibration method and device of active noise reduction system of wearable device, storage medium and terminal Download PDFInfo
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1785—Methods, e.g. algorithms; Devices
- G10K11/17853—Methods, e.g. algorithms; Devices of the filter
- G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1787—General system configurations
- G10K11/17879—General system configurations using both a reference signal and an error signal
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R1/00—Details of transducers, loudspeakers or microphones
- H04R1/10—Earpieces; Attachments therefor ; Earphones; Monophonic headphones
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- G—PHYSICS
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- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/10—Applications
- G10K2210/108—Communication systems, e.g. where useful sound is kept and noise is cancelled
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- H04R2460/00—Details of hearing devices, i.e. of ear- or headphones covered by H04R1/10 or H04R5/033 but not provided for in any of their subgroups, or of hearing aids covered by H04R25/00 but not provided for in any of its subgroups
- H04R2460/01—Hearing devices using active noise cancellation
Abstract
A calibration method and device for an active noise reduction system of a wearable device, a storage medium and a terminal are provided, the wearable device comprises the active noise reduction system, and the method comprises the following steps: starting an active noise reduction system, controlling an audio playing module to play a noise test signal, and acquiring the noise test signal through a sound acquisition module; calculating a secondary path estimate of the active noise reduction system; stopping playing the noise test signal, closing the active noise reduction system, playing the environmental noise through an external playing module, and measuring the passive noise reduction level; configuring initial parameters of an active noise reduction system, wherein the initial parameters comprise secondary path estimation; starting an active noise reduction system, controlling an audio playing module to play a noise reduction signal and measuring the active noise reduction level; and when the difference value between the active noise reduction level and the passive noise reduction level is in the preset value interval and the coefficient of the filter is converged, acquiring the coefficient of the filter as a calibration coefficient. Therefore, the debugging period of the active noise reduction system can be shortened, and the debugging efficiency is improved.
Description
Technical Field
The invention relates to the field of audio signal processing, in particular to a calibration method and device of an active noise reduction system of wearable equipment, a storage medium and a terminal.
Background
In a wearable device such as an earphone for performing audio processing, an Active Noise Cancellation (ANC) system is usually included, and the Active Noise reduction system plays a sound wave in a phase opposite to an ambient Noise through a speaker to neutralize and cancel the Noise, thereby achieving a Noise reduction effect. Most wearable devices in the market at present need to determine reference coefficients of a filter in an active noise reduction system through debugging before leaving a factory so as to calibrate the filter according to the reference coefficients.
However, in the actual debugging process, because of the influence of factors such as models and wearing modes of wearable devices such as earphones, the reference coefficient can be determined only by carrying out mold opening for many times and a large amount of debugging verification, and the debugging period can reach months. If the calibration coefficients obtained by iteration before a certain debugging and verifying link is found to be not suitable, the debugging is required to be started from the beginning, and the period is further prolonged.
In conclusion, the traditional active noise reduction system has long debugging period and low debugging efficiency.
Disclosure of Invention
The invention solves the technical problem of how to shorten the debugging period of the active noise reduction system and improve the debugging efficiency.
To solve the above problem, an embodiment of the present invention provides a calibration method for an active noise reduction system of a wearable device, where the wearable device includes the active noise reduction system, and the active noise reduction system includes: the audio playing module is used for playing the input audio signal; the sound acquisition module is used for acquiring the audio signal played by the audio playing module or the audio signal played by the external playing module; the filter is used for filtering the audio signal collected by the sound collection module to obtain a noise reduction signal; an adaptation module for adjusting coefficients of the filter; characterized in that the method comprises: starting the active noise reduction system, controlling the audio playing module to play a noise test signal, and acquiring the noise test signal through the sound acquisition module; calculating a secondary path estimate for the active noise reduction system; stopping playing the noise test signal, closing the active noise reduction system, playing environmental noise through an external playing module, and measuring the passive noise reduction level of the wearable device; configuring initial parameters of the active noise reduction system, the initial parameters including the secondary path estimate; starting the active noise reduction system, controlling the audio playing module to play the noise reduction signal and measuring the active noise reduction level of the wearable equipment; and when the difference value between the active noise reduction level and the passive noise reduction level is in a preset value interval and the coefficient of the filter is converged, acquiring the coefficient of the filter as a calibration coefficient, wherein the calibration coefficient is used for calibrating the filter.
Optionally, the calculating a secondary path estimate of the active noise reduction system includes: and estimating a transfer function of a secondary channel according to the noise test signal played by the audio playing module and the noise test signal collected by the sound collecting module to obtain the estimation of the secondary channel.
Optionally, the sound collection module includes an error microphone, and calculates a secondary path estimate of the active noise reduction system according to the following formula: s (n +1) ═ S (n) +2 μ e (n) spk (n); wherein n is used for representing time, the value of n is a positive integer, S (n) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n, S (n +1) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n +1, μ is a fixed step factor, spk (n) is a noise test signal played by the audio playing module, and e (n) is a middle error signal; wherein e (n) ═ d (n) -spk (n) ST(n); d (n) noise test signal collected by the error microphone, ST(n) is the transpose of S (n).
Optionally, the sound collection module further includes a reference microphone, the filter is an FxLMS filter, and a filter coefficient convergence formula is as follows: w (n +1) ═ W (n) +2 μm (n) x ^ (n); wherein n is used for representing time, the value of n is a positive integer, W (n) is a filter coefficient corresponding to the time n, W (n +1) is a filter coefficient corresponding to the time n +1, mu is a fixed step size factor, m (n) is a middle error signal, and x ^ (n) is a signal acquired by the reference microphoneSignals after the signals pass through the secondary path; wherein x ^ (n) ═ x (n) ST(n); (n) a secondary path estimate obtained by adaptive modeling of a secondary path corresponding to the time n, and x (n) a signal acquired by the reference microphone; m (n) ═ f (n) + x (n) WT(n); f (n) is x (n) the signal after passing through the main path, WT(n) is the transpose of W (n).
Optionally, the passive noise reduction level of the wearable device is measured by measuring a passive noise reduction curve of the wearable device, and the active noise reduction level of the wearable device is measured by measuring an active noise reduction curve of the wearable device.
Optionally, the filter includes a single feedforward filter, a single feedback filter, or a feedforward plus feedback hybrid filter.
Optionally, after obtaining the coefficient of the filter as a calibration coefficient, the method further includes: and acquiring multiple groups of calibration coefficients of the multiple wearable devices, and determining comprehensive coefficients for calibrating the active noise reduction modules of the multiple wearable devices according to the multiple groups of calibration coefficients.
Optionally, the audio playing module is controlled to play the noise test signal in a silencing environment.
The embodiment of the present invention further provides a calibration apparatus for an active noise reduction system of a wearable device, where the wearable device includes the active noise reduction system, and the active noise reduction system includes: the audio playing module is used for playing the input audio signal; the sound acquisition module is used for acquiring the audio signal played by the audio playing module or the audio signal played by the external playing module; the filter is used for filtering the signal acquired by the sound acquisition module to obtain a noise reduction signal; an adaptation module for adjusting coefficients of the filter; the calibration device includes: the noise test signal playing module is used for starting the active noise reduction system, controlling the audio playing module to play a noise test signal and acquiring the noise test signal through the sound acquisition module; a secondary path estimation module for calculating a secondary path estimate for the active noise reduction system; the passive noise reduction curve measurement module is used for stopping playing the noise test signal, closing the active noise reduction system, playing the environmental noise through an external playing module, and measuring the passive noise reduction level of the wearable device; an initial parameter configuration module configured to configure initial parameters of the active noise reduction system, the initial parameters including the secondary path estimate; the active noise reduction level measurement module is used for starting the active noise reduction system, controlling the audio playing module to play the noise reduction signal and measuring the active noise reduction level of the wearable device; and the calibration coefficient acquisition module is used for acquiring a coefficient of the filter as a calibration coefficient when the difference value between the active noise reduction level and the passive noise reduction level is within a preset value interval and the coefficient of the filter is converged, wherein the calibration coefficient is used for calibrating the filter.
A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method of calibrating an active noise reduction system of any wearable device.
A computer device comprising calibration means of an active noise reduction system of a wearable device or comprising a memory having stored thereon a computer program executable on said processor and a processor which, when running said computer program, performs the steps of any of the methods of calibration of an active noise reduction system of a wearable device.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
according to the calibration method of the active noise reduction system of the wearable device, provided by the embodiment of the invention, the secondary channel estimation, the convergence step length, the order and the like of the active noise reduction system corresponding to the same model of the wearable device are used as initial parameters, the difference value between the active noise reduction level and the passive noise reduction level of the wearable device of the same model is controlled to be located in the preset value range, and when the filter coefficient is converged, the calibration coefficient of the filter corresponding to the model is obtained and is used for carrying out production line calibration on the wearable device of the model. Therefore, one-key adaptive filter coefficient calibration can be realized under the same wearable device model, and compared with the complex process of transfer function measurement, calculation and verification in the traditional calibration, the efficiency of filter design is greatly improved, and the debugging period is shortened. Furthermore, the calibration coefficient is obtained based on the active noise reduction level and the passive noise reduction level of the same model wearable device and is not influenced by the type of the filter, so that the method can be suitable for a single feedforward filter or a single feedback filter or a feedforward and feedback hybrid filter, and the application range is wider.
Optionally, the transfer function of the secondary path may be estimated in an offline manner to obtain a secondary path estimate of the active noise reduction system; alternatively, the secondary path estimate for the active noise reduction system may be calculated online by adaptive modeling of the secondary path.
Further, wearable devices with different models can be replaced, the calibration method is executed, calibration coefficients corresponding to the models are obtained, and an average value or a truncated average value and the like are obtained by combining multiple sets of calibration coefficients, so that filter coefficients capable of reflecting the models of the wearable devices are obtained, and therefore the filter coefficients for calibration are more universal.
Drawings
Fig. 1 is a schematic structural diagram of an active noise reduction system according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a calibration method of an active noise reduction system of a wearable device according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of another active noise reduction system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a calibration apparatus of an active noise reduction system of a wearable device according to an embodiment of the present invention.
Detailed Description
It will be appreciated by those skilled in the art that the active noise reduction system of wearable devices such as headphones is generally adapted to calculate a transfer function to obtain a reference coefficient for active noise reduction, and if the active noise reduction system of headphones employs a feedforward filter, the measured main path transfer function is represented by p (z) and the measured secondary path transfer function is represented by s (z), so that the filter coefficient w (z) ═ p (z)/s (z) can be deduced. In the actual debugging process, parameters such as P (z), S (z) and the like are greatly influenced by factors such as the difference of wearable equipment models such as earphones and the like and the difference of user wearing modes, so that the reference coefficient can be determined only by carrying out mold opening for many times and a large amount of debugging verification, the debugging period is long, and the debugging efficiency is low.
In order to shorten the debugging period of the active noise reduction system and improve the debugging efficiency, the embodiment of the invention provides a wearable device comprising the active noise reduction system 10. Referring to fig. 1, fig. 1 is a schematic structural diagram of an active noise reduction system 10. The active noise reduction system 10 includes: the audio playing module 101 is configured to play an input audio signal. A sound collection module 102, configured to collect an audio signal played by the audio playing module 101 or an audio signal played by the external playing module 11. A filter 103 for filtering the audio signal collected by the sound collection module 102 to generate a noise reduction signal, wherein the filter may include a single feedforward filter or a single feedback filter or a feedforward and feedback mixed filter. An adaptation module 104 for adjusting coefficients of said filter 103.
The wearable device is a wearable device to be subjected to audio processing, and may include various types of earphones (such as a headset, an in-ear earphone, a bluetooth earphone, and the like), and other wearable devices including an active noise reduction system. The audio playing module 101 in the active noise reduction system 10 may be configured to play the generated noise reduction signal to implement an active noise reduction function; the audio playing module 101 may also be configured to play other sound signals, for example, a sound signal sent by a control module (such as a Central Processing Unit (CPU) of the wearable device) to the audio playing module 101. The audio playing module 101 may include a speaker, a loudspeaker, and the like.
Referring to fig. 2, fig. 2 is a calibration method of an active noise reduction system of a wearable device according to an embodiment of the present invention, where the calibration method relates to the active noise reduction system 10 shown in fig. 1, the calibration method is executed by a computer or other terminal to obtain a calibration coefficient during a filter design stage of the active noise reduction system, the terminal can be connected to the wearable device to control the active noise reduction system to be turned on and off to execute the method, and the terminal is further connected to an external play module 11 to control the external play module 11 to play ambient noise. The method includes steps S201 to S205, which are detailed below.
Optionally, before the step S201 is started, a wearable device (such as an earphone or the like) is correctly worn on the artificial ear, where the correct wearing refers to that the angle at which the wearable device is worn is correct, and it is ensured that the tightness between the wearable device and the artificial ear is good.
Step S201, starting the active noise reduction system 10, controlling the audio playing module 101 to play a noise test signal, and acquiring the noise test signal through the sound acquisition module 102.
The noise test signal may be a white noise test signal, a pink noise test signal, or other preset noise signals. Optionally, the terminal sends a noise test signal to the wearable device, the control module of the wearable device controls the audio playing module 101 to play the noise test signal, and the sound collecting module 102 can collect the noise test signal.
Step S202, calculating a secondary path estimate of the active noise reduction system 10.
Wherein the secondary path estimate is an estimate of the secondary path transfer function of the active noise reduction system 10.
Step S203, stopping playing the noise test signal, closing the active noise reduction system 10, playing the ambient noise through the external playing module 11, and measuring the passive noise reduction level of the wearable device.
The terminal controls the audio playing module 101 of the wearable device to stop playing the noise test signal, and turns off the active noise reduction system 10, that is, the active noise reduction function of the wearable device is deactivated, and at this time, the wearable device can only perform passive noise reduction. The passive noise reduction refers to the noise reduction capability of the wearable device after the active noise reduction system is turned off, and mainly refers to the isolation of ears from external noise through sound insulation materials.
Optionally, the passive noise reduction level of the wearable device is measured by measuring a passive noise reduction curve of the wearable device. Optionally, the passive noise reduction curve is measured by an external measurement device, and the passive noise reduction level may also be a parameter capable of reflecting the passive noise reduction capability of the wearable device, where the parameter is related to factors such as the material and shape of the sound insulation material.
Optionally, if there is a need for multi-scene noise reduction, the ambient noise of different scenes may be played, so that the calibration coefficients of the filter 103 in different scenes can be obtained.
Step S204, configuring initial parameters of the active noise reduction system 10, where the initial parameters include the secondary path estimation.
Optionally, the initial parameter may further include preset values such as a convergence step size and an order, in addition to the secondary path estimation. The secondary path estimate is configured to the adaptation module 104.
Step S205, starting the active noise reduction system 10, controlling the audio playing module 101 to play the noise reduction signal and measuring the active noise reduction level of the wearable device.
After the active noise reduction system 10 is started, the adaptive module 104 is enabled, and the adaptive module 104 can adjust the coefficients of the filter 103 according to the noise reduction condition of the wearable device. The active noise reduction level refers to the noise reduction capability of the wearable device after the active noise reduction system 10 is turned on, and optionally, the noise reduction level may be a parameter capable of reflecting the noise reduction capability of the wearable device after the active noise reduction system 10 is turned on.
Optionally, the active noise reduction level of the wearable device is measured by measuring an active noise reduction curve of the wearable device. Wherein the active noise reduction curve is a noise reduction curve of the wearable device measured by an external measuring device after the active noise reduction system 10 is turned on.
Optionally, the active noise reduction curve and the passive noise reduction curve are curves drawn by taking frequency as an abscissa and taking noise reduction volume as an ordinate.
Step S206, when the difference between the active noise reduction level and the passive noise reduction level is within a preset value range and the coefficient of the filter 103 is converged, acquiring the coefficient of the filter 103 as a calibration coefficient, where the calibration coefficient is used to calibrate the filter 103.
The preset value interval is used for representing the noise reduction capability that the active noise reduction system 10 needs to achieve, and the specific value of the preset value interval is set according to experimental data. The coefficient of the filter 103 converges, that is, the capability of the filter 103 tends to be stable, and the filter coefficient at this time is acquired as a calibration coefficient. The calibration coefficients may be obtained when the wearable device is produced or tested, and the filters of the active noise reduction system 10 of the wearable device may be calibrated.
By the method shown in fig. 2, the secondary path estimation, the convergence step length, the order number and the like of the active noise reduction system 10 corresponding to the same model of the wearable device are used as initial parameters, the difference value between the active noise reduction level and the passive noise reduction level of the wearable device of the same model is controlled to be within the preset value range, and when the filter coefficient is converged, the calibration coefficient of the filter corresponding to the model is obtained for performing production line calibration on the wearable device of the model. Therefore, one-key adaptive filter coefficient calibration can be realized under the same wearable device model, and compared with the complex process of transfer function measurement, calculation and verification in the traditional calibration, the efficiency of filter design is greatly improved, and the debugging period is shortened. Furthermore, the calibration coefficient is obtained based on the active noise reduction level and the passive noise reduction level of the same model wearable device and is not influenced by the type of the filter, so that the method can be suitable for a single feedforward filter or a single feedback filter or a feedforward and feedback hybrid filter, and the application range is wider.
In one embodiment, the calculating the secondary path estimate of the active noise reduction system 10 in step S202 may include: and estimating a transfer function of a secondary channel according to the noise test signal played by the audio playing module and the noise test signal collected by the sound collecting module to obtain the estimation of the secondary channel.
Optionally, the noise test signal played by the audio playing module 101 and the noise test signal collected by the sound collection module 102 are respectively obtained. And carrying out wiener estimation on the transfer function of the secondary path to obtain the secondary path estimation.
Optionally, performing wiener estimation on the transfer function of the secondary path to obtain the secondary path estimate, including: measuring a noise test signal (namely, an input signal of the secondary path, denoted as y (n)) played by the audio playing module 101 and a noise test signal (namely, an output signal of the secondary path, denoted as y' (n)) acquired by the sound acquisition module 102; obtaining an autocorrelation matrix R of an input signal of a secondary path according to the following equation (1)yy(ii) a Obtaining a cross-correlation matrix r of an input signal of the secondary path and an output signal of the secondary path according to the following formula (2)yy’(ii) a The secondary path estimate S' (n) is obtained according to the following equation (3).
Ryy＝E[y(n)yT(n)] (1)
ryy’＝E[y(n)y’(n)] (2)
S’(n)＝Ryy -1ryy’ (3)
Wherein E () is the mathematical expectation of finding the value in parentheses, yTAnd (n) is a transposed matrix of y (n), and the value of n is a positive integer.
The solution of the present embodiment can calculate the secondary path estimation value in an off-line manner, and the secondary path estimation value does not change with the change of the adaptive module 104.
In an embodiment, referring to fig. 1 and fig. 3, fig. 3 is a schematic structural diagram of another active noise reduction system 30, and the sound collection module 102 in fig. 1 includes an error microphone 1021 for collecting an audio signal played by the audio playing module 101. Calculating the secondary path estimate of the active noise reduction system as described in step S302 is implemented according to the following equation (4):
S(n+1)＝S(n)+2μe(n)spk(n) (4)
wherein e (n) in the formula (4) is the intermediate error signal, and the calculation method is as the following formula (5):
e(n)＝d(n)-spk(n)ST(n) (5)
wherein n is used for representing time, the value of n is a positive integer, S (n) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n, S (n +1) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n +1, μ is a fixed step factor, spk (n) is a noise test signal played by the audio playing module, d (n) is a noise test signal acquired by the error microphone, and S (n) is a noise test signal acquired by the error microphoneT(n) is the transpose of S (n).
The adaptive modeling is a process of estimating a transfer function of the secondary path by using an adaptive filter, and the adaptive modeling method can refer to the existing modeling method and is not described in detail here. In this embodiment, the estimation of the secondary path of the active noise reduction system can be calculated on-line according to the above formula by adaptive modeling of the secondary path.
In one embodiment, with continued reference to fig. 3, the sound collection module 102 further includes a reference microphone 1022, and the reference microphone 1022 is used for collecting a sound signal generated outside the wearable device, such as an audio signal played by the external playing module 11 or a noise signal in the environment. The filter is a filtered-x least mean square (FxLMS) filter, and the coefficient convergence of the filter is as follows in formula (6):
W(n+1)＝W(n)+2μm(n)x^(n) (6)
x ^ (n) is the signal acquired by the reference microphone after the signal passes through the secondary path, which can be obtained according to the formula (7):
x^(n)＝x(n)ST(n) (7)
m (n) is the intermediate error signal, which can be derived from equation (8):
m(n)＝f(n)+x(n)WT(n) (8)
wherein n is used for representing time, the value of n is a positive integer, W (n) is a filter coefficient corresponding to the time n, W (n +1) is a filter coefficient corresponding to the time n +1, mu is a fixed step size factor, f (n) is a signal of x (n) after passing through a main channel, and W (n) is a signal of a constant step size factorT(n) isW (n), s (n) is a secondary path estimate obtained by adaptive modeling of a secondary path corresponding to time n, and x (n) is a signal acquired by the reference microphone.
It should be noted that the filter 103 in the active noise reduction system 10 may adopt other algorithms, such as minimum Mean Square error (LMS), Normalized minimum Mean Square error (NLMS), variable step size-based minimum Mean Square error (VSLMS), and the like, and the corresponding convergence manner thereof may refer to the existing scheme and is not described herein again.
Optionally, the active noise reduction system 30 shown in fig. 3 may further include down-sampling modules 1 to 301, configured to down-sample the audio signal acquired by the error microphone 1021, and output the down-sampled audio signal to the filter 103; the active noise reduction system 30 may further include a down-sampling module 2-302, configured to down-sample the audio signal acquired by the reference microphone 1022, and output the down-sampled audio signal to the filter 103; down-sampling module 1 and down-sampling module 2 down-sample the received signal to the operating sample rate of filter 103 and adaptation module 104. The active noise reduction system 30 may further include an up-sampling module 303, configured to up-sample the generated noise reduction signal, and send the up-sampled audio signal to the audio playing module 101, so as to play the audio signal in a sound cavity (such as a sound cavity of an earphone) of the wearable device, where the audio signal interferes with noise transmitted into the sound cavity from an external environment, so as to achieve the purpose of active noise reduction.
Please refer to the related description in fig. 1 for other modules of the active noise reduction system 30 shown in fig. 3, which are not repeated herein.
In one embodiment, after acquiring the coefficients of the filter as the calibration coefficients in step S206 in fig. 2, the method further includes: and acquiring multiple groups of calibration coefficients of the multiple wearable devices, and determining comprehensive coefficients for calibrating the active noise reduction modules of the multiple wearable devices according to the multiple groups of calibration coefficients.
In order to make the calibrated filter coefficients have universality for the models of the wearable devices, the models can be replaced, and the steps S201 to S206 are repeated, so that finally, a plurality of sets of calibration coefficients corresponding to the obtained models are obtained, and an average value or a truncated average value and the like are obtained by combining the plurality of sets of calibration coefficients, so that the filter coefficients reflecting the models of the wearable devices, that is, the comprehensive coefficients, can be obtained.
In one embodiment, the audio playback module 101 is controlled to play the noise test signal in a mute environment. That is, step S201 is performed in a sound damping room or sound damping box environment to avoid the influence of external interfering sound signals.
Referring to fig. 4, an embodiment of the present invention further provides a calibration apparatus 40 for an active noise reduction system of a wearable device, including: a noise test signal playing module 401, configured to start the active noise reduction system, control the audio playing module to play a noise test signal, and acquire the noise test signal through the sound acquisition module; a secondary path estimation module 402 for calculating a secondary path estimate for the active noise reduction system; a passive noise reduction curve measurement module 403, configured to stop playing the noise test signal, close the active noise reduction system, play an ambient noise through an external playing module, and measure a passive noise reduction level of the wearable device; an initial parameter configuration module 404 configured to configure initial parameters of the active noise reduction system, the initial parameters including the secondary path estimate; an active noise reduction level measurement module 405, configured to start the active noise reduction system, control the audio playing module to play the noise reduction signal, and measure an active noise reduction level of the wearable device; a calibration coefficient obtaining module 406, configured to obtain a coefficient of the filter as a calibration coefficient when a difference between the active noise reduction level and the passive noise reduction level is within a preset value range and the coefficient of the filter is converged, where the calibration coefficient is used to calibrate the filter.
The calibration device 40 shown in fig. 4 refers to the active noise reduction system 10 shown in fig. 1 or the active noise reduction system 30 shown in fig. 3, and the calibration device 40 may be disposed on a terminal side of a computer or the like.
For more details about the working principle and the working mode of the calibration apparatus 40 of the active noise reduction system of the wearable device, reference may be made to fig. 1 to 3 for the related description of the calibration method of the active noise reduction system of the wearable device, and details are not repeated here.
In a specific implementation, the calibration device 40 of the active noise reduction System of the wearable device may correspond to a Chip of a calibration function of the active noise reduction System of the wearable device, or correspond to a Chip having a data processing function, such as a System-On-a-Chip (SOC), a baseband Chip, or the like; or the chip module corresponds to the UE and comprises a calibration function chip with an active noise reduction system of the wearable device; or to a chip module having a chip with a data processing function, or to a terminal.
In a specific implementation, each module/unit included in each apparatus and product described in the foregoing embodiments may be a software module/unit, may also be a hardware module/unit, or may also be a part of a software module/unit and a part of a hardware module/unit.
For example, for each device or product applied to or integrated into a chip, each module/unit included in the device or product may be implemented by hardware such as a circuit, or at least a part of the module/unit may be implemented by a software program running on a processor integrated within the chip, and the rest (if any) part of the module/unit may be implemented by hardware such as a circuit; for each device or product applied to or integrated with the chip module, each module/unit included in the device or product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules/units may be implemented by using a software program running on a processor integrated within the chip module, and the rest (if any) of the modules/units may be implemented by using hardware such as a circuit; for each device and product applied to or integrated in the terminal, each module/unit included in the device and product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least part of the modules/units may be implemented by using a software program running on a processor integrated in the terminal, and the rest (if any) part of the modules/units may be implemented by using hardware such as a circuit.
Embodiments of the present invention further provide a storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the calibration method of the active noise reduction system of the wearable device in any one of fig. 1 to 3. The storage medium may be a computer-readable storage medium, and may include, for example, a non-volatile (non-volatile) or non-transitory (non-transitory) memory, and may further include an optical disc, a mechanical hard disk, a solid state hard disk, and the like.
The embodiment of the present invention further provides a terminal, which includes a calibration apparatus of an active noise reduction system of a wearable device, or includes a memory and a processor, where the memory stores a computer program executable on the processor, and the processor executes the steps of the calibration method of the active noise reduction system of the wearable device in any one of fig. 1 to 3 when executing the computer program.
Specifically, in the embodiment of the present invention, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will also be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example and not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (enhanced SDRAM), SDRAM (SLDRAM), synchlink DRAM (SLDRAM), and direct bus RAM (DR RAM).
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document indicates that the former and latter related objects are in an "or" relationship.
The "plurality" appearing in the embodiments of the present application means two or more.
The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application.
The term "connect" in the embodiments of the present application refers to various connection manners, such as direct connection or indirect connection, to implement communication between devices, which is not limited in this embodiment of the present application.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (11)
1. A method of calibrating an active noise reduction system of a wearable device, the wearable device including an active noise reduction system, the active noise reduction system comprising: the audio playing module is used for playing the input audio signal; the sound acquisition module is used for acquiring the audio signal played by the audio playing module or the audio signal played by the external playing module; the filter is used for filtering the audio signal collected by the sound collection module to obtain a noise reduction signal; an adaptation module for adjusting coefficients of the filter; characterized in that the method comprises:
starting the active noise reduction system, controlling the audio playing module to play a noise test signal, and acquiring the noise test signal through the sound acquisition module;
calculating a secondary path estimate for the active noise reduction system;
stopping playing the noise test signal, closing the active noise reduction system, playing environmental noise through an external playing module, and measuring the passive noise reduction level of the wearable device;
configuring initial parameters of the active noise reduction system, the initial parameters including the secondary path estimate;
starting the active noise reduction system, controlling the audio playing module to play the noise reduction signal and measuring the active noise reduction level of the wearable equipment;
and when the difference value between the active noise reduction level and the passive noise reduction level is in a preset value interval and the coefficient of the filter is converged, acquiring the coefficient of the filter as a calibration coefficient, wherein the calibration coefficient is used for calibrating the filter.
2. The method of claim 1, wherein the calculating the secondary path estimate for the active noise reduction system comprises:
and estimating a transfer function of a secondary channel according to the noise test signal played by the audio playing module and the noise test signal collected by the sound collecting module to obtain the estimation of the secondary channel.
3. The method of claim 1, wherein the sound collection module comprises an error microphone, and wherein the secondary path estimate for the active noise reduction system is calculated according to the following equation:
S(n+1)＝S(n)+2μe(n)spk(n)；
wherein n is used for representing time, the value of n is a positive integer, S (n) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n, S (n +1) is a secondary path estimation obtained by adaptive modeling of a secondary path corresponding to the time n +1, μ is a fixed step factor, spk (n) is a noise test signal played by the audio playing module, and e (n) is a middle error signal;
wherein e (n) ═ d (n) -spk (n) ST(n)；
d (n) noise test signal collected by the error microphone, ST(n) is the transpose of S (n).
4. A method according to any one of claims 1 to 3, wherein the sound collection module further comprises a reference microphone, the filter is an FxLMS filter, and the filter coefficients converge as follows:
W(n+1)＝W(n)+2μm(n)x^(n)；
wherein n is used for representing time, the value of n is a positive integer, W (n) is a filter coefficient corresponding to the time n, W (n +1) is a filter coefficient corresponding to the time n +1, mu is a fixed step size factor, m (n) is a middle error signal, and x ^ (n) is a signal acquired by the reference microphone after the signal passes through a secondary path;
wherein x ^ (n) ═ x (n) ST(n)；
(n) a secondary path estimate obtained by adaptive modeling of a secondary path corresponding to the time n, and x (n) a signal acquired by the reference microphone;
m(n)＝f(n)+x(n)WT(n)；
f (n) is x (n) the signal after passing through the main path, WT(n) is the transpose of W (n).
5. The method of claim 1, wherein the passive noise reduction level of the wearable device is measured by measuring a passive noise reduction curve of the wearable device, and wherein the active noise reduction level of the wearable device is measured by measuring an active noise reduction curve of the wearable device.
6. The method of claim 1, wherein the filter comprises a single feedforward filter, a single feedback filter, or a feedforward plus feedback hybrid filter.
7. The method of claim 1, wherein after obtaining the coefficients of the filter as calibration coefficients, further comprising:
and acquiring multiple groups of calibration coefficients of the multiple wearable devices, and determining comprehensive coefficients for calibrating the active noise reduction modules of the multiple wearable devices according to the multiple groups of calibration coefficients.
8. The method of claim 1, wherein the audio playback module is controlled to play the noise test signal in a mute environment.
9. A calibration apparatus for an active noise reduction system of a wearable device, the wearable device including an active noise reduction system, the active noise reduction system comprising: the audio playing module is used for playing the input audio signal; the sound acquisition module is used for acquiring the audio signal played by the audio playing module or the audio signal played by the external playing module; the filter is used for filtering the signal acquired by the sound acquisition module to obtain a noise reduction signal; an adaptation module for adjusting coefficients of the filter; characterized in that the calibration device comprises:
the noise test signal playing module is used for starting the active noise reduction system, controlling the audio playing module to play a noise test signal and acquiring the noise test signal through the sound acquisition module;
a secondary path estimation module for calculating a secondary path estimate for the active noise reduction system;
the passive noise reduction curve measurement module is used for stopping playing the noise test signal, closing the active noise reduction system, playing the environmental noise through an external playing module, and measuring the passive noise reduction level of the wearable device;
an initial parameter configuration module configured to configure initial parameters of the active noise reduction system, the initial parameters including the secondary path estimate;
the active noise reduction level measurement module is used for starting the active noise reduction system, controlling the audio playing module to play the noise reduction signal and measuring the active noise reduction level of the wearable device;
and the calibration coefficient acquisition module is used for acquiring a coefficient of the filter as a calibration coefficient when the difference value between the active noise reduction level and the passive noise reduction level is within a preset value interval and the coefficient of the filter is converged, wherein the calibration coefficient is used for calibrating the filter.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method according to any of the claims 1 to 8.
11. A computer arrangement comprising an apparatus as claimed in claim 9, or comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor, when executing the computer program, performs the steps of the method of any one of claims 1 to 8.
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