CN111800688A - Active noise reduction method and device, electronic equipment and storage medium - Google Patents

Active noise reduction method and device, electronic equipment and storage medium Download PDF

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Publication number
CN111800688A
CN111800688A CN202010213933.4A CN202010213933A CN111800688A CN 111800688 A CN111800688 A CN 111800688A CN 202010213933 A CN202010213933 A CN 202010213933A CN 111800688 A CN111800688 A CN 111800688A
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signal
iteration
noise reduction
proximity sensor
signal value
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CN202010213933.4A
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CN111800688B (en
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刘涛
朱彪
王丽
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Shenzhen Horn Audio Co Ltd
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Shenzhen Horn Audio Co Ltd
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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
    • H04R1/1083—Reduction of ambient noise
    • 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
    • G10K11/1781—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 characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
    • G10K11/17821—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 characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
    • G10K11/17825—Error signals
    • 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

Abstract

The application is suitable for the technical field of noise reduction, and provides an active noise reduction method, which comprises the following steps: acquiring a signal value acquired by a proximity sensor, a noise signal value acquired by a feedforward microphone and an error signal value acquired by a feedback microphone; controlling an iterative operation mode of the adaptive filtering algorithm according to a signal value acquired by the proximity sensor and a noise signal value acquired by the feedforward microphone; updating a filter coefficient of each iteration of the adaptive filter algorithm according to a signal value acquired by the proximity sensor, a noise signal acquired by the feedforward microphone and an error signal acquired by the feedback microphone; when the adaptive filtering algorithm is in the normal iteration mode, generating a noise reduction signal through the adaptive filtering algorithm according to the updated filtering coefficient to reduce the noise of the noise signal; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy. The noise reduction can be carried out according to the filter coefficient updated in real time at the wearing position of the earphone, and the noise reduction effect is improved.

Description

Active noise reduction method and device, electronic equipment and storage medium
Technical Field
The present application belongs to the field of noise reduction technologies, and in particular, to an active noise reduction method and apparatus, an electronic device, and a storage medium.
Background
Noise reduction techniques are also becoming increasingly important because of the large amount of noise present in social environments that interferes with people's lives and tasks. In the field of noise reduction of earphones, two mainstream noise elimination modes of active noise reduction and passive noise reduction are generally available. The passive noise reduction mainly utilizes the materials of the earphone to resist and absorb noise, has better noise reduction capability on a high-frequency part, and has poorer noise reduction effect on medium and low frequencies. Therefore, active noise reduction technology is generally adopted for noise reduction of medium and low frequencies.
In the active noise reduction technology in the field of earphone noise reduction, a filtering algorithm mainly adopts factory-adjusted filtering parameters, and has poor applicability in the noise reduction process, so that the noise reduction effect is sharply reduced or serious problems such as extra abnormal noise and the like occur, and the noise reduction effect is poor.
Disclosure of Invention
The embodiment of the application provides an active noise reduction method, an active noise reduction device, electronic equipment and a storage medium, and aims to solve the serious problems that the noise reduction effect is sharply reduced or extra abnormal noise occurs and the like due to poor applicability of the existing earphone in the active noise reduction process, so that the noise reduction effect is poor.
In a first aspect, an embodiment of the present application provides an active noise reduction method, which is applied to a headset that includes at least one proximity sensor, a feedforward microphone, and a feedback microphone:
the active noise reduction method comprises the following steps:
acquiring a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone and an error signal value acquired by the feedback microphone;
controlling the iterative operation mode of the adaptive filtering algorithm according to the signal value acquired by the proximity sensor and the noise signal value acquired by the feedforward microphone; wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode;
updating the filter coefficient of each iteration of the adaptive filter algorithm according to the signal value acquired by the proximity sensor, the noise signal acquired by the feedforward microphone and the error signal acquired by the feedback microphone;
when the adaptive filtering algorithm is in the normal iteration mode, generating a noise reduction signal through the adaptive filtering algorithm according to the updated filtering coefficient to reduce the noise of the noise signal; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
In a second aspect, the present application provides an active noise reduction apparatus, which is applied to a headset, where the headset includes at least one proximity sensor, a feedforward microphone, and a feedback microphone;
the active noise reduction device includes:
the acquisition module is used for acquiring a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone and an error signal value acquired by the feedback microphone;
the control module is used for controlling the iterative operation mode of the adaptive filtering algorithm according to the signal value acquired by the proximity sensor and the noise signal value acquired by the feedforward microphone; wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode;
the updating module is used for updating a filter coefficient used by the adaptive filter algorithm in each iteration according to a signal value acquired by the proximity sensor, a noise signal acquired by the feedforward microphone and an error signal acquired by the feedback microphone;
the noise reduction module is used for generating a noise reduction signal through the adaptive filtering algorithm according to the updated filtering coefficient to reduce the noise of the noise signal when the adaptive filtering algorithm is in the normal iteration mode; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the active noise reduction method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the active noise reduction method are implemented.
In a fifth aspect, an embodiment of the present application provides a computer program product, which, when run on an electronic device, causes the electronic device to perform the active noise reduction method according to any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: according to the signal value acquired by the proximity sensor, the method and the device control the iterative operation mode of the adaptive filtering algorithm. The filter coefficient of each iteration of the adaptive filter algorithm can be updated according to the signal value acquired by the proximity sensor, and the noise signal is denoised by generating a denoising signal through the adaptive filter algorithm according to the updated filter coefficient. The signal value acquired by the proximity sensor can reflect the wearing position of the earphone, so that the noise can be reduced according to the filter coefficient updated in real time at the wearing position of the earphone, and the noise reduction effect is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an earphone according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an active noise reduction method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of an active noise reduction method according to another embodiment of the present application;
FIG. 4a is a schematic diagram of an earphone feedforward adaptive filtering active noise reduction system according to another embodiment of the present application;
FIG. 4b is a schematic diagram of a hybrid active noise reduction system with feedforward adaptive filtering and feedback adaptive filtering for headphones according to another embodiment of the present application;
fig. 5 is a schematic structural diagram of an active noise reduction device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In order to explain the technical means described in the present application, the following examples are given below.
Example one
Referring to fig. 1, an embodiment of the present application provides an earphone including at least one proximity sensor 11, a feedforward microphone 12, and a feedback microphone 13;
in one embodiment, the earphone further comprises a sound emitter and a housing, wherein the sound emitter is used for emitting sound to the earphone.
In application, the proximity sensor may be an optical sensor, a capacitive sensor or other sensor having a proximity determination function. The sound emitter may specifically be a horn or a loudspeaker. Active noise reduction can be realized by collecting a noise source by using a microphone and generating a noise reduction signal according to the noise source by an acoustic destructive interference principle, so that the purpose of noise reduction is achieved, and the microphone for collecting the noise source is called a feedforward microphone at the moment. The feedforward microphone can be arranged in the rear cavity of the earphone shell in advance, and the voice collecting end faces the outer side of the earphone. At least one proximity sensor can be preset on the outer surface of the earphone shell or in the shell and used for detecting the wearing state of the earphone, the proximity sensor can be arranged at a position close to the auricle when the earphone is worn, the specific position can be set according to actual requirements, and limitation is not performed. The processor may be any device capable of implementing data processing and control functions, such as a Central Processing Unit (CPU) of a single chip microcomputer.
Referring to fig. 2, an embodiment of the present application provides an active noise reduction method applied to the earphone in the embodiment corresponding to fig. 1, including:
step S201, acquiring a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone, and an error signal value acquired by the feedback microphone.
In application, the signal value collected by the proximity sensor can reflect the wearing position of the earphone, and the signal value collected by the proximity sensor is obtained. When the signal value collected by the proximity sensor is larger, the earphone is worn closer to the auricle. When the signal value collected by the proximity sensor is smaller, the earphone is worn farther away from the auricle. The noise signal collected by the feedforward microphone is converted into a corresponding noise signal value through an analog-to-digital conversion. The feedback microphone can be arranged in the front cavity of the earphone and used for collecting error noise after noise reduction processing and carrying out analog-to-digital conversion to obtain a corresponding error signal value.
Step S202, controlling an iterative operation mode of the adaptive filtering algorithm according to the signal value acquired by the proximity sensor;
wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode.
In application, because the signal value acquired by the proximity sensor reflects the wearing position of the earphone, the adaptive filtering algorithm is controlled to be in a pause iteration mode, and an iteration mode and a normal iteration mode are stopped according to the wearing position of the earphone. The pause iteration mode is to stop iteration noise reduction temporarily, the iteration noise reduction is resumed after the iteration noise reduction condition is met, and the initial filter coefficient for resuming the iteration noise reduction is the filter coefficient kept before the pause iteration, so that new iteration is not required to be restarted. The iteration stopping mode is that after iteration is stopped, iteration noise reduction is restarted after the iteration noise reduction condition is met.
In one embodiment, the controlling the operation mode of the iteration of the adaptive filtering algorithm according to the signal value collected by the proximity sensor comprises: detecting the time-varying rate of a signal of a user wearing the earphone within a preset time according to the signal value acquired by the proximity sensor; when the time-varying rate of the signal is greater than the preset maximum time-varying rate of the signal, controlling the adaptive filtering algorithm to enter a pause iteration mode; and when the time-varying rate of the signal is smaller than the preset maximum time-varying rate of the signal, controlling the adaptive filtering algorithm to recover the normal iteration mode according to the filtering coefficient used before the suspended iteration mode. If the change rate of the signal value acquired by the proximity sensor in the preset time is large, the position of the earphone is changed all the time, at the moment, the adaptive filtering algorithm is controlled to pause iteration and maintain the filtering coefficient of the adaptive filtering algorithm at the moment, and when the change rate of the signal value acquired by the proximity sensor in the preset time is smaller than the preset value, the filtering coefficient stored in the process of pausing iteration is utilized to control the adaptive filtering algorithm to resume iterative noise reduction.
In application, if the change rate of a signal value acquired by a proximity sensor in a preset time is large, the correlation degree between the noise signal value acquired by a feedforward microphone and a real noise signal value is also changed all the time, a secondary acoustic propagation channel shakes, if continuous iterative noise reduction is carried out in the rapid shaking process, the filter coefficient is suddenly changed or exceeds the range, abnormal noise is generated, the judgment is carried out according to the change rate of the signal acquired by the proximity sensor, iteration is stopped in advance, and the generation of the situation can be effectively reduced. And when the fast jitter disappears, it takes longer to return to the normal noise reduction level because the filter coefficients were abrupt or out of range before. Therefore, when the time-varying rate of the signal is greater than the preset maximum time-varying rate of the signal, the noise reduction is stopped, the noise reduction stability can be ensured, and because the initial value keeps the last value, new iteration does not need to be restarted, and the convergence time is reduced.
In one embodiment, the controlling the operation mode of the iteration of the adaptive filtering algorithm according to the signal value collected by the proximity sensor further comprises: presetting the signal value of the proximity sensor signal into N threshold value intervals according to the size, wherein the first signal threshold value interval is a preset signal strongest interval, and the Nth signal threshold value interval is a preset signal weakest interval; and when the signal value acquired by the proximity sensor is smaller than the minimum value of the Nth signal threshold interval, controlling the adaptive filtering algorithm to enter an iteration stopping mode so as to suspend the active noise reduction function, and preventing larger abnormal noise caused by the fact that convergence cannot be achieved all the time. The minimum value of the Nth signal threshold interval is the lower limit value of the Nth signal threshold interval.
And step S203, updating the filter coefficient of each iteration of the adaptive filter algorithm according to the signal value acquired by the proximity sensor, the noise signal acquired by the feedforward microphone and the error signal acquired by the feedback microphone.
In application, the filter coefficient used in each iterative noise reduction of the adaptive filter algorithm may be updated according to the signal value acquired by the proximity sensor, the noise signal acquired by the feedforward microphone, and the error signal acquired by the feedback microphone.
Specifically, according to the noise signal, the error noise after the noise reduction processing is obtained through the feedback microphone, and the filtering algorithm weight coefficient is continuously adjusted to reduce the residual error until convergence. Updating the filter coefficients used by the adaptive filter algorithm for each iteration is implemented, for example, by using an LMS filter algorithm or an FxLMS filter algorithm.
Step S204, when the adaptive filtering algorithm is in the normal iteration mode, according to the updated filtering coefficient, generating a noise reduction signal through the adaptive filtering algorithm to reduce the noise of the noise signal;
wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
In application, the adaptive filtering algorithm generates a noise reduction signal through the adaptive filtering algorithm using the updated filter coefficients when the adaptive filtering algorithm is in the normal iterative noise reduction mode. The preset filtering algorithm may be an adaptive filtering algorithm such as FxLMS or LMS, or other adaptive filtering algorithms.
According to the signal value acquired by the proximity sensor, the method and the device control the iterative operation mode of the adaptive filtering algorithm. The filter coefficient of each iteration of the adaptive filter algorithm can be updated according to the signal value acquired by the proximity sensor, and the noise signal is denoised by generating a denoising signal through the adaptive filter algorithm according to the updated filter coefficient. The signal value acquired by the proximity sensor can reflect the wearing position of the earphone, so that the noise can be reduced according to the filter coefficient updated in real time at the wearing position of the earphone, and the noise reduction effect is improved.
Example two
The embodiment of the present application provides an active noise reduction method, which is further described in the first embodiment, and reference may be specifically made to the related description of the first embodiment where the same or similar to the first embodiment, and details are not described herein again. Referring to fig. 3, the step S203 includes:
step S301, updating the filter coefficient in real time through the FxLMS adaptive filter algorithm with variable leakage factor and variable iteration step length, and updating the variable leakage factor and the variable iteration step length in the FxLMS adaptive filter algorithm in real time according to the signal value acquired by the proximity sensor.
In application, the filter coefficient can be updated through the FxLMS adaptive filter algorithm of the variable leakage factor variable iteration step, and the variable leakage factor and the iteration step in the FxLMS adaptive filter algorithm are updated in real time according to the signal value acquired by the proximity sensor.
In one embodiment, when a signal threshold interval in which a signal value acquired by a current proximity sensor is located changes, an initial coefficient and a corresponding secondary channel model used by an adaptive filtering algorithm corresponding to the threshold interval in which the signal value acquired by the current proximity sensor is located are acquired, and iteration of the filtering coefficient is restarted. The signal value of the proximity sensor signal is divided into N threshold intervals, such as a first signal threshold interval, a second signal threshold interval, …, and an nth signal threshold interval, according to magnitude. The first signal threshold interval is a preset signal strongest interval, and the Nth signal threshold interval is a preset signal weakest interval.
In the application, the signal value acquired by the proximity sensor is monitored in real time, and when the signal value is in any one of N threshold value intervals, the initial coefficient of the filtering algorithm corresponding to the interval in which the signal value is located and the corresponding secondary channel model are obtained. And when the position signal value detected at the current moment is in any one of N threshold value intervals and does not belong to the interval at the last moment, restarting a round of iteration updating of the filter coefficient according to the corresponding initial coefficient of the filter algorithm and the corresponding secondary channel model, and performing corresponding iteration noise reduction through the adaptive filter algorithm according to the iteration updating of the filter coefficient each time. The current time may represent a current time period, such as a current second or a current two seconds, and the like, the previous time may represent a time period before the current time period, and a specific time period of the current time may be preset according to an actual requirement, which is not limited herein.
In the application, in the active noise reduction process, if the same secondary channel module is used to process the noise signal regardless of how the noise is reduced, the noise reduction effect and the noise reduction stability are easily affected. The secondary channel model corresponding to each of the N threshold intervals can prompt a user to rotate or move different wearing positions through terminal equipment in advance, each position stays for a certain time, a signal is sent out at an earphone sound generator at the moment, a microphone receives the signal at the same time, a new estimated secondary channel model is generated through a self-adaptive filtering algorithm, and a corresponding proximity sensor signal value is recorded. Alternatively, the initial filter coefficient and the secondary channel model corresponding to each subinterval may be preset.
In one embodiment, the updating the variable leakage factor and the iteration step in the FxLMS adaptive filtering algorithm in real time according to the signal value acquired by the proximity sensor includes: calculating a variable iteration step size u (n) and a variable leakage factor v (n) of the nth iteration according to a signal value P (n) acquired by the proximity sensor in the nth iteration and a noise signal value Xf (n) acquired by the feedforward microphone in the nth iteration; wherein n ≧ 1 and is an integer.
In application, the calculation formula for calculating the variable iteration step size u (n) of the nth iteration is as follows:
u(n)＝max{umax*(P(n)-Pmin)/(Pmax-Pmin)/(q+E(Xf(n)^2)),umin/(q+E(Xf(n)^2)}；
the calculation formula for calculating the variable leakage factor v (n) of the nth iteration is as follows:
v(n)＝1–umax*rmax*(Pmax-P(n))/(Pmax-Pmin)；
wherein u (n) is a variable iteration step size of the nth iteration, and umaxTo preset the maximum original step length, uminFor presetting a minimum original step length, P (n) is a signal value acquired by a current nth-moment proximity sensor, and PmaxIs the maximum value of the first signal threshold interval, PminIs the minimum value of the Nth signal threshold interval, q is a preset minimum value (the prevention denominator is 0), E (Xf (N) 2) is the power average value of the noise signal after the noise signal is processed by the secondary channel model, v (N) is the leakage factor of the nth iteration, rmaxIs a preset maximum bias factor.
In application, the variable leakage factor and the iteration step size are updated with signal values acquired by the proximity sensor. When the wearing position of the earphone is improved, the leakage factor is close to 1, and the iteration step length is increased, so that the maximum noise reduction effect can be ensured while the fastest convergence is ensured; and when the wearing position becomes worse, the leakage factor is gradually reduced, and the iteration step length is reduced at the same time, so that a certain noise reduction effect is ensured.
In one embodiment, the updating of the filter coefficient in real time by the FxLMS adaptive filtering algorithm with the variable iteration step size of the variable leakage factor includes: calculating a variable iteration step length u (n) and a variable leakage factor v (n) of the latest iteration according to the real-time coefficient Wf (n) of the nth iteration filtering, a noise signal value Xf (n) collected by a feedforward microphone in the nth iteration and an error signal value e (n) collected by a feedback microphone, and calculating the real-time coefficient Wf (n +1) of the (n +1) th iteration filtering;
the calculation formula for calculating the real-time coefficient Wf (n +1) of the (n +1) th iterative filtering is as follows:
Wf(n+1)＝v(n)*Wf(n)+u(n)*Xf(n)*e(n)
wherein Wf (n +1) is a real-time coefficient of the (n +1) th iteration filtering, v (n) is a variable leakage factor of the calculated latest iteration, Wf (n) is a real-time coefficient of the (n) th iteration filtering, u (n) is a variable iteration step of the calculated latest iteration, xf (n) is a noise signal value collected by the feedforward microphone at the nth iteration, and e (n) is an error signal value collected by the feedback microphone.
Step S302, when the signal value collected by the proximity sensor is larger than the upper limit value of the first signal threshold interval, updating the filter coefficient to a factory default value.
In application, when the detected signal value is larger than the upper limit value of the first signal threshold interval, the distance between the earphone and the auricle is smaller than the preset nearest distance, the filtering coefficient is directly updated to a factory default value at the moment, the earphone is well worn, and noise reduction is directly carried out according to the factory default value.
In one embodiment, fig. 4a is a schematic diagram of a headphone feed-forward adaptive filtering active noise reduction system.
In the embodiment of the application, the noise reduction principle is feedforward adaptive filtering active noise reduction, and the feedforward adaptive filter is an FxLMS adaptive filter 1 with variable leakage factors and variable iteration step sizes. And updating the variable leakage factor and the iteration step length in the feedforward filter in real time according to the signal value acquired by the proximity sensor. According to the signal value acquired by the proximity sensor, the noise signal value acquired by the feedforward microphone and the error signal value acquired by the feedback microphone, the coefficient Wf of the feedforward filter is updated in real time, noise can be reduced according to the feedforward filter coefficient updated in real time, and the noise reduction effect is improved; and on the other hand, the iteration control module controls the iterative operation mode of the feedforward adaptive filtering algorithm according to the signal value acquired by the proximity sensor, so that the stability of the noise reduction effect is improved.
In one embodiment, fig. 4b is a schematic diagram of a hybrid active noise reduction system of headphone feedforward adaptive filtering plus feedback adaptive filtering according to another embodiment of the present application.
In the embodiment of the application, the noise reduction principle is a mixed active noise reduction of feedforward adaptive filtering and feedback adaptive filtering, the feedforward adaptive filter is an FxLMS adaptive filter 1 with variable leakage factor and variable iteration step, and the feedback adaptive filter is an FxLMS adaptive filter 2 with variable leakage factor and variable iteration step. And simultaneously updating the variable leakage factor and the iteration step length in the feedforward filter and the feedback filter in real time according to the signal value acquired by the proximity sensor, wherein the variable leakage factor and the iteration step length can have different sizes, because some preset parameters in calculation formulas of the variable leakage factor and the iteration step length can be different. And updating the coefficient Wf of the feedforward filter in real time according to the signal value acquired by the proximity sensor, the noise signal value acquired by the feedforward microphone and the error signal value acquired by the feedback microphone. And updating the feedback filter coefficient Wb in real time according to the signal value acquired by the proximity sensor and the error signal value acquired by the feedback microphone. The feedback adaptive filter does not use the signal collected by the feedforward microphone, but calculates a virtual feedforward microphone signal from the signal passing through the articulator and the error signal value collected by the feedback microphone. The virtual feedforward microphone signal can replace the signal collected by the real feedforward microphone in the filter coefficient updating formula and the variable leakage factor and iteration step updating formula. The 3 sub-channel prediction models in fig. 4b are all consistent and can be updated uniformly according to the signal values collected by the proximity sensor. The system performs mixed noise reduction according to the feedforward filter coefficient and the feedback filter coefficient which are updated in real time, so that the noise reduction effect is greatly improved; and on the other hand, the iteration control module simultaneously controls the iterative operation modes of the feedforward adaptive filter and the feedback adaptive filter according to the signal value acquired by the proximity sensor, so that the stability of the noise reduction effect is improved.
EXAMPLE III
Fig. 5 shows a block diagram of an active noise reduction apparatus provided in an embodiment of the present application for performing the active noise reduction method, where the active noise reduction apparatus may be integrated in a headset or other electronic device, and when the active noise reduction apparatus is a headset, the headset includes at least one proximity sensor, a feedforward microphone, and a feedback microphone. In one embodiment, the headset further comprises a processor, a sound emitter, and a housing. The embodiment of the present application does not set any limitation to a specific type of the electronic device, and only a part related to the embodiment of the present application is shown for convenience of explanation.
Referring to fig. 5, the active noise reduction apparatus 500 provided in this embodiment includes:
an obtaining module 501, configured to obtain a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone, and an error signal value acquired by the feedback microphone;
a control module 502, configured to control an iterative operation mode of the adaptive filtering algorithm according to a signal value acquired by the proximity sensor and a noise signal value acquired by the feedforward microphone; wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode;
an updating module 503, configured to update a filter coefficient of each iteration of the adaptive filtering algorithm according to a signal value acquired by the proximity sensor, a noise signal acquired by the feedforward microphone, and an error signal acquired by the feedback microphone;
a noise reduction module 504, configured to generate a noise reduction signal through the adaptive filtering algorithm according to the updated filter coefficient when the adaptive filtering algorithm is in the normal iteration mode, so as to reduce noise of the noise signal; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
In one embodiment, the control module 502 includes:
the detection unit is used for detecting the time-varying rate of a signal of the user wearing the earphone within a preset time according to the signal value acquired by the proximity sensor;
the first control unit is used for controlling the self-adaptive filtering algorithm to enter a pause iteration mode when the time change rate of the signal is greater than the preset maximum time change rate of the signal;
and the second control unit is used for controlling the self-adaptive filtering algorithm to recover the normal iteration mode according to the filtering coefficient used before the suspended iteration mode when the time change rate of the signal is smaller than the preset maximum time change rate of the signal.
In one embodiment, the control module 502 further comprises:
the pre-dividing unit is used for pre-dividing the signal value of the proximity sensor signal into N threshold value intervals according to the size, wherein the first signal threshold value interval is a preset signal strongest interval, and the Nth signal threshold value interval is a preset signal weakest interval;
and the third control unit is used for controlling the adaptive filtering algorithm to enter an iteration stopping mode to suspend the active noise reduction function when the signal value acquired by the proximity sensor is smaller than the minimum value of the Nth signal threshold interval.
In one embodiment, the update module comprises:
the first updating unit is used for updating the filter coefficient in real time through the FxLMS adaptive filter algorithm of the variable leakage factor variable iteration step length, and updating the variable leakage factor and the variable iteration step length in the FxLMS adaptive filter algorithm in real time according to the signal value acquired by the proximity sensor;
and the second updating unit is used for updating the filter coefficient to a factory default value when the signal value acquired by the proximity sensor is greater than the upper limit value of the first signal threshold interval.
The active noise reduction device further comprises:
and the re-iteration module is used for acquiring an initial coefficient used by the adaptive filtering algorithm corresponding to the threshold interval in which the signal value acquired by the current proximity sensor is located and a corresponding secondary channel model when the signal threshold interval in which the signal value acquired by the current proximity sensor is located is changed, and restarting the iteration of the filtering coefficient.
In one embodiment, the updating the variable leakage factor and the iteration step in the FxLMS adaptive filtering algorithm in real time according to the signal value acquired by the proximity sensor includes: calculating a variable iteration step u (n) and a variable leakage factor v (n) of the nth iteration according to the signal value acquired by the proximity sensor during the nth iteration and a noise signal value Xf (n) acquired by the feedforward microphone during the nth iteration; wherein n ≧ 1 and is an integer.
In one embodiment, the updating of the filter coefficient in real time by the FxLMS adaptive filtering algorithm with the variable iteration step size of the variable leakage factor includes: calculating a variable iteration step length u (n) and a variable leakage factor v (n) of the latest iteration according to the real-time coefficient Wf (n) of the nth iteration filtering, a noise signal value Xf (n) collected by a feedforward microphone in the nth iteration and an error signal value e (n) collected by a feedback microphone, and calculating the real-time coefficient Wf (n +1) of the (n +1) th iteration filtering;
the calculation formula for calculating the real-time coefficient Wf (n +1) of the (n +1) th iterative filtering is as follows:
Wf(n+1)＝v(n)*Wf(n)+u(n)*Xf(n)*e(n)
wherein Wf (n +1) is a real-time coefficient of the (n +1) th iteration filtering, v (n) is a variable leakage factor of the calculated latest iteration, Wf (n) is a real-time coefficient of the (n) th iteration filtering, u (n) is a variable iteration step of the calculated latest iteration, xf (n) is a noise signal value collected by the feedforward microphone at the nth iteration, and e (n) is an error signal value collected by the feedback microphone.
According to the signal value acquired by the proximity sensor and the noise signal value acquired by the feedforward microphone, the embodiment of the application controls the iterative operation mode of the adaptive filtering algorithm. The filter coefficient of each iteration of the adaptive filter algorithm can be updated according to the signal value acquired by the proximity sensor, and the noise signal is denoised by generating a denoising signal through the adaptive filter algorithm according to the updated filter coefficient. The signal value acquired by the proximity sensor can reflect the wearing position of the earphone, so that the noise can be reduced according to the filter coefficient updated in real time at the wearing position of the earphone, and the noise reduction effect is improved.
EXAMPLE five
As shown in fig. 6, an embodiment of the present invention further provides an electronic device 600, which includes: a processor 601, a memory 602, and a computer program 603, such as an active noise reduction program, stored in the memory 602 and executable on the processor 601. The processor 601, when executing the computer program 603, implements the steps in the above-described active noise reduction method embodiments, for example, the method steps in embodiment one, embodiment two, and/or embodiment three. The processor 601, when executing the computer program 603, implements the functions of the modules in the above-described device embodiments, such as the functions of the modules 501 to 504 shown in fig. 5.
Illustratively, the computer program 603 may be partitioned into one or more modules that are stored in the memory 602 and executed by the processor 601 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program 603 in the electronic device 600. For example, the computer program 603 may be divided into an obtaining module, a control module, an updating module, and a noise reduction module, and specific functions of the modules are described in the fourth embodiment, which is not described herein again.
The electronic device 600 may be an electronic device such as a headset or other electronic device. The terminal device may include, but is not limited to, a processor 601 memory 602. Those skilled in the art will appreciate that fig. 5 is merely an example of an electronic device 600 and does not constitute a limitation of electronic device 600 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 601 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 602 may be an internal storage unit of the electronic device 600, such as a hard disk or a memory of the electronic device 600. The memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 600. Further, the memory 602 may also include both an internal storage unit and an external storage device of the electronic device 600. The memory 602 is used for storing the computer programs and other programs and data required by the terminal device. The memory 602 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (10)

1. An active noise reduction method is applied to a headset, wherein the headset comprises at least one proximity sensor, a feedforward microphone and a feedback microphone;
the active noise reduction method comprises the following steps:
acquiring a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone and an error signal value acquired by the feedback microphone;
controlling an iterative operation mode of an adaptive filtering algorithm according to the signal value acquired by the proximity sensor; wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode;
updating a filter coefficient used by the adaptive filter algorithm in each iteration according to a signal value acquired by the proximity sensor, a noise signal acquired by the feedforward microphone and an error signal acquired by the feedback microphone;
when the adaptive filtering algorithm is in the normal iteration mode, generating a noise reduction signal through the adaptive filtering algorithm according to the updated filtering coefficient to reduce the noise of the noise signal; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
2. The active noise reduction method of claim 1, wherein the controlling the operation mode of the adaptive filtering algorithm iteration according to the signal values collected by the proximity sensor comprises:
detecting the time-varying rate of a signal of a user wearing the earphone within a preset time according to the signal value acquired by the proximity sensor;
when the time-varying rate of the signal is greater than the preset maximum time-varying rate of the signal, controlling the adaptive filtering algorithm to enter a pause iteration mode;
and when the time-varying rate of the signal is smaller than the preset maximum time-varying rate of the signal, controlling the adaptive filtering algorithm to recover the normal iteration mode according to the filtering coefficient used before the suspended iteration mode.
3. The active noise reduction method of claim 1, wherein controlling an operation mode of the adaptive filtering algorithm iteration according to the signal values collected by the proximity sensor further comprises:
presetting the signal value of the proximity sensor signal into N threshold value intervals according to the size, wherein the first signal threshold value interval is a preset signal strongest interval, and the Nth signal threshold value interval is a preset signal weakest interval;
and when the signal value acquired by the proximity sensor is smaller than the minimum value of the Nth signal threshold interval, controlling the adaptive filtering algorithm to enter an iteration stopping mode so as to suspend the active noise reduction function.
4. The active noise reduction method of claim 1, wherein updating the filter coefficients used by the adaptive filtering algorithm for each iteration based on the proximity sensor signal values, the noise signal collected by the feedforward microphone, and the error signal collected by the feedback microphone comprises:
updating a filter coefficient in real time through an FxLMS adaptive filter algorithm with variable leakage factors and variable iteration step length, and updating the variable leakage factors and the variable iteration step length in the FxLMS adaptive filter algorithm in real time according to a signal value acquired by the proximity sensor;
and when the signal value acquired by the proximity sensor is larger than the upper limit value of the first signal threshold interval, updating the filter coefficient to a factory default value.
5. The active noise reduction method of claim 4, comprising:
when the signal threshold interval where the signal value acquired by the current proximity sensor is located changes, the initial coefficient and the corresponding secondary channel model used by the adaptive filtering algorithm corresponding to the threshold interval where the signal value acquired by the current proximity sensor is located are acquired, and iteration of the filtering coefficient is restarted.
6. The active noise reduction method according to claim 4, wherein the updating the variable leakage factor and the iteration step in the FxLMS adaptive filtering algorithm in real time according to the signal value collected by the proximity sensor comprises:
calculating a variable iteration step length u (n) and a variable leakage factor v (n) of the nth iteration according to a signal value acquired by the proximity sensor during the nth iteration and a noise signal value Xf (n) acquired by the feedforward microphone during the nth iteration; wherein n ≧ 1 and is an integer.
7. The active noise reduction method according to any of claims 4 to 6, wherein the updating of the filter coefficients in real time by the FxLMS adaptive filtering algorithm with the variable iteration step size of the variable leakage factor comprises:
calculating a variable iteration step length u (n) and a variable leakage factor v (n) of the latest iteration according to the real-time coefficient Wf (n) of the nth iteration filtering, a noise signal value Xf (n) collected by a feedforward microphone in the nth iteration and an error signal value e (n) collected by a feedback microphone, and calculating the real-time coefficient Wf (n +1) of the (n +1) th iteration filtering;
the calculation formula for calculating the real-time coefficient Wf (n +1) of the (n +1) th iterative filtering is as follows:
Wf(n+1)＝v(n)*Wf(n)+u(n)*Xf(n)*e(n)
wherein Wf (n +1) is a real-time coefficient of the (n +1) th iteration filtering, v (n) is a variable leakage factor of the calculated latest iteration, Wf (n) is a real-time coefficient of the (n) th iteration filtering, u (n) is a variable iteration step of the calculated latest iteration, xf (n) is a noise signal value collected by the feedforward microphone at the nth iteration, and e (n) is an error signal value collected by the feedback microphone.
8. An active noise reduction device applied to a headset, wherein the headset comprises at least one proximity sensor, a feedforward microphone and a feedback microphone;
the active noise reduction device includes:
the acquisition module is used for acquiring a signal value acquired by the proximity sensor, a noise signal value acquired by the feedforward microphone and an error signal value acquired by the feedback microphone;
the control module is used for controlling an iterative operation mode of the adaptive filtering algorithm according to the signal value acquired by the proximity sensor; wherein the operation mode comprises a pause iteration mode, a stop iteration mode and a normal iteration mode;
the updating module is used for updating the filter coefficient of each iteration of the adaptive filter algorithm according to the signal value acquired by the proximity sensor, the noise signal acquired by the feedforward microphone and the error signal acquired by the feedback microphone;
the noise reduction module is used for generating a noise reduction signal through the adaptive filtering algorithm according to the updated filtering coefficient and reducing the noise of the noise signal when the adaptive filtering algorithm is in the normal iteration mode; wherein the noise reduction signal and the noise signal have opposite phases and the same frequency and energy.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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