Active noise reduction method, apparatus, device, and medium

Abstract

This invention relates to the field of noise reduction in voice devices, and discloses an active noise reduction method, apparatus, device, and medium. The method includes determining whether a music mode is enabled, filtering the received noise information accordingly, playing sound to cancel out external noise, obtaining residual noise information, directly inputting the residual noise information into the SPSA algorithm for calculation, and updating the weight coefficients of the second filter. If the music mode is enabled, the residual noise information also contains music information, therefore the music information needs to be separately removed by the first filter. This achieves the ability to cancel out external noise and improve noise reduction performance in music mode, call mode, and other modes.

Description

Technical Field

[0001] This invention relates to the field of noise reduction in voice devices, and more particularly to an active noise reduction method, apparatus, device, and medium. Background Technology

[0002] With the development of technology, voice devices have become increasingly common in people's daily lives. When people use voice devices to listen to audio information such as voice and music, external environmental noise can also pass through the voice device and be heard by the ears. Especially when the external environmental noise is complex, it can seriously affect people's listening experience. Therefore, noise reduction processing is needed to reduce the impact of external noise on voice devices. In addition to reducing noise interference through the physical structure of the device, voice devices can also use active noise reduction to reduce the impact of noise information.

[0003] Current active noise reduction in voice devices is based on the LMS or FX-LMS algorithm to calculate the amplitude of noise information. Both require adjusting the weight coefficients of the adaptive filter. The weight coefficients S of the adaptive filter are easily affected by the external environment and its own working state. When the affected weight coefficients S are used for calculation during active noise reduction, it will lead to inaccurate calculation of the amplitude of external noise by the voice device, thus affecting the active noise reduction effect of the voice device. Summary of the Invention

[0004] The main objective of this invention is to solve the technical problems of inaccurate amplitude calculation of noise information in different environments and poor noise reduction effect of voice devices.

[0005] The first aspect of this invention provides an active noise reduction method, comprising:

[0006] Acquire external noise and external noise information obtained after the external noise passes through an acoustic path;

[0007] The external noise is processed according to the preset noise processing device to obtain noise processing information;

[0008] Determine if the preset music mode is enabled;

[0009] If not enabled, the noise processing information will be played.

[0010] The first residual noise information is obtained by canceling out the external noise information with the noise processing information;

[0011] Based on the SPSA algorithm, the weight coefficients of the noise processing device are updated using the first residual noise information;

[0012] If enabled, mixed information is played, including the noise processing information and music information;

[0013] The second residual noise information is obtained by canceling out the mixed information with the external noise information, wherein the music information is included in the second residual noise information;

[0014] The music information in the second residual noise information is filtered according to the preset first filter to obtain the third residual noise information;

[0015] Based on the SPSA algorithm, the weight coefficients of the noise processing device are updated using the third residual noise information.

[0016] Optionally, in a first implementation of the first aspect of the present invention, the noise processing apparatus includes a random generator for generating a random vector of synchronization disturbance and a second filter.

[0017] Optionally, in a second implementation of the first aspect of the present invention, the step of processing the external noise according to the preset noise processing device to obtain noise processing information includes:

[0018] The external noise is convolved with the synchronous perturbation random vector to obtain convolution information;

[0019] The received external noise is filtered according to the second filter, and corresponding filtering information is generated;

[0020] The convolutional information and the filtering information are superimposed to obtain noise processing information.

[0021] Optionally, in a third implementation of the first aspect of the invention, the synchronization perturbation random vector follows a Bernoulli distribution Δ. k ,in,

[0022] Optionally, in a fourth implementation of the first aspect of the present invention, updating the weight coefficients of the noise processing device using the first residual noise information based on the SPSA algorithm includes:

[0023] The first residual noise information is used as the cost function, and the cost function is substituted into the gradient estimation calculation to obtain the gradient Δ. w (n);

[0024] The gradient Δ w (n) Substitute this into the coefficient update equation to update the weight coefficients of the second filter. The specific coefficient update equation is W. (n+1) =W (n) -a k *Δw (n), where the W (n) The a is the current weight coefficient of the second filter. k The step size.

[0025] Optionally, in a fifth implementation of the first aspect of the present invention, using the first residual noise information as a cost function includes:

[0026] The specific expression for the cost function is as follows:

[0027] Where λ = 1, 2, 3...n, the e (n) The first residual noise information is J(u(n)), which is the estimated value of the cost function.

[0028] Optionally, in a sixth implementation of the first aspect of the present invention, the step of substituting the cost function into the gradient estimation calculation to obtain the gradient Δ w (n) includes:

[0029] The specific expression for calculating the gradient estimation is as follows:

[0030] Wherein, c k This is the gain coefficient of the synchronization disturbance vector.

[0031] A second aspect of the present invention provides an active noise cancellation device, comprising:

[0032] The acquisition module is used to acquire external noise and external noise information obtained after the external noise passes through an acoustic path;

[0033] The processing module is used to process the external noise according to the preset noise processing device to obtain noise processing information;

[0034] The monitoring module is used to determine whether the preset music mode is enabled;

[0035] The first judgment module is used to play the noise processing information if it is not enabled.

[0036] The first cancellation module is used to cancel the external noise information with the noise processing information to obtain the first residual noise information;

[0037] The first update module is used to update the weight coefficients of the noise processing device based on the SPSA algorithm and utilizing the first residual noise information.

[0038] The second judgment module is used to play mixed information if it is enabled, wherein the mixed information includes the noise processing information and the music information;

[0039] The second cancellation module is used to cancel the mixed information with the external noise information to obtain second residual noise information, wherein the music information is included in the second residual noise information;

[0040] The filtering module is used to filter the music information in the second residual noise information according to the preset first filter to obtain the third residual noise information;

[0041] The second update module is used to update the weight coefficients of the noise processing device based on the SPSA algorithm and the third residual noise information.

[0042] A third aspect of the present invention provides an active noise cancellation device, the active noise cancellation device comprising: a memory and at least one processor, the memory storing instructions, and the memory and the at least one processor being interconnected via a circuit;

[0043] The at least one processor invokes the instructions in the memory to cause the device to perform the above-described active noise reduction method.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium for a voice device, the computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described active noise reduction method.

[0045] In the technical solution provided by this invention, by determining whether the music mode is on, the received noise information is filtered accordingly, and the sound used to cancel the external noise information is played to obtain residual noise information. The residual noise information is directly fed into the SPSA algorithm for calculation to update the weight coefficients of the second filter. If the music mode is on, the residual noise information also contains music information, so the music information needs to be removed separately by the first filter. This achieves the ability to cancel external noise and improve the noise reduction effect in music mode, call mode, or other modes. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the first embodiment of the active noise reduction method in this invention;

[0047] Figure 2 This is a schematic diagram of the second embodiment of the active noise reduction method in this invention;

[0048] Figure 3 This is a schematic diagram of the third embodiment of the active noise reduction method in this invention;

[0049] Figure 4 This is a schematic diagram of one embodiment of the active noise cancellation device in this invention;

[0050] Figure 5 This is a schematic diagram of one embodiment of the active noise cancellation device in this invention. Detailed Implementation

[0051] This invention provides an active noise reduction method, system, device, equipment, and storage medium.

[0052] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 -Appendix Figure 3 One embodiment of the active noise reduction method in this invention includes:

[0054] 101. Acquire external noise and external noise information obtained after the external noise passes through the acoustic path;

[0055] 102. Based on the preset noise processing device, external noise is processed to obtain noise processing information;

[0056] Furthermore, the noise processing device includes a random generator for generating a random vector of synchronization disturbance and a second filter.

[0057] Furthermore, step 102 can also be performed as follows:

[0058] 1021. Convolve the external noise with the synchronous perturbation random vector to obtain the convolution information;

[0059] Among them, the synchronous perturbation random vector needs to follow a Bernoulli distribution Δ k ,in,

[0060] 1022. The received external noise is filtered according to the second filter, and corresponding filtering information is generated;

[0061] 1023. The convolution information and the filtering information are superimposed to obtain the noise processing information.

[0062] 103. Determine if the preset music mode is enabled;

[0063] In this embodiment, the music mode is a feature of the device to which this method is applied. The device is equipped with a music device for playing music and also has a built-in monitoring unit for monitoring whether the music device is turned on and for monitoring real-time sound effects.

[0064] 104. If not enabled, noise reduction information will be played;

[0065] In this embodiment, when the music device is not turned on, only external noise information is circulating. The weight coefficients of the built-in second filter need to be calculated in advance. Therefore, after the device to which this method is applied is turned on, white noise is played first to simulate the external noise circulating inside. Since the weight coefficients of the second filter are blank when it is used for the first time, the white noise needs to circulate inside twice so that the SPSA algorithm can obtain the white noise information and the residual noise information calculated from the previous white noise to update the weight coefficients of the second filter. Therefore, the weight coefficients are applied to the noise reduction calculation of the actual received external noise.

[0066] 105. Based on the cancellation of the mixed information and the external noise information, the second residual noise information is obtained, wherein the music information is included in the second residual noise information;

[0067] In this embodiment, xn is defined as external noise. This external noise passes through acoustic path P to obtain dn, specifically expressed as: dn = xn * P. The external noise is also convolved with the synchronization perturbation random vector Q generated by the random generator and passed through a second filter to obtain convolution information q and filtered information y, respectively, specifically expressed as: q = xn * Q; y = xn * W. Since the convolution information q and filtered information y need to be superimposed before being received by the microphone and played through the speaker, and the preset weight coefficient of the microphone is S, the superimposed convolution information q and filtered information y, when played out, yield noise processing information y', specifically expressed as: y' = (q + y) * S. Finally, the noise processing information y' is canceled out with dn obtained by the external noise through acoustic path P to obtain the first residual noise information e. (n) The specific expression is: e (n) =dn-y'.

[0068] 106. Based on the SPSA algorithm, the noise processing device is updated using the first residual noise information;

[0069] Furthermore, step 106 can also be performed as follows:

[0070] 1061. Using the first residual noise information as the cost function, substitute the cost function into the gradient estimation calculation to obtain the gradient Δ. w (n);

[0071] Furthermore, the specific expression for "using the first residual noise information as the cost function" is as follows:

[0072] Where λ = 1, 2, 3...n, e (n) J(u(n)) represents the first residual noise information, and J(u(n)) represents the estimated value of the cost function.

[0073] Furthermore, "substitute the cost function into the gradient estimation calculation to obtain the gradient Δ". w The specific expression for (n) is: Among them, c k This is the gain coefficient of the synchronization disturbance vector.

[0074] 1062. The gradient Δ w Substituting (n) into the coefficient update equation, the weight coefficients of the second filter are updated. The specific coefficient update equation is W. (n+1) =W (n) -a k *Δ w (n), where W (n) Let a be the current weight coefficients of the second filter. k The step size.

[0075] 107. If enabled, mixed information will be played, which includes noise processing information and music information;

[0076] In this embodiment, the music device starts playing music information mc, which, along with convolution information q and filtering information y, is received by the microphone and played out through the speaker. The specific expression is: y' = (q + y - mc) * S.

[0077] 108. Based on the noise processing information, the external noise information is canceled out to obtain the second residual noise information; 109. Based on the preset first filter, the music information in the second residual noise information is filtered to obtain the third residual noise information;

[0078] In this embodiment, since the music information is played to the user along with the second residual noise information, the less the second residual noise information, the more lossless and clear the music information heard by the user. Therefore, before updating the weight coefficients of the second filter using the SPSA algorithm, the music information needs to be removed, leaving only the second residual noise information as the sole update parameter for the SPSA algorithm. Before this, the weight coefficients S_HAT of the first filter need to be pre-set. The first filter is a virtual filter set up to estimate the secondary path of the microphone. Gaussian white noise vn is played, the microphone receives the Gaussian white noise vn and plays a sound y' that is opposite to the external noise data through the speaker, where y' = vn * S, and y' is the same as the external noise information. The noise is canceled out, resulting in residual noise e, where e = dn - y'. Gaussian white noise vn also passes through the first filter to obtain vn', where vn' = vn * S_HAT. Finally, y' needs to be completely removed, leaving external noise information for LMS algorithm calculation. Therefore, the filtered residual noise is s_e, where s_e = dn - y' + vn'. Since the filtered residual noise s_e is not completely equivalent to dn, s_e and Gaussian white noise vn need to be directly calculated together through the LMS algorithm to update the weight coefficients of the first filter. The specific expression is: S_HAT' = S_HAT + mu * s_e * vn', where S_HAT' is the updated weight coefficient of the first filter, and mu is the step size.

[0079] After obtaining the latest weight coefficients of the first filter, the noise reduction process begins. In step 107, the sound y' played by the speaker to cancel out external noise information is obtained. After canceling out the external noise information, the second residual noise information e2 is obtained, where e2 = dn - y'. e2 contains not only residual noise but also music information. Therefore, while being heard by the user, it is also filtered by the built-in first filter. The first filter directly receives the music information mc and plays the opposite sound mc', where mc' = mc * S_HAT. When the second residual noise information e2 is received internally, the music information mc within it needs to be removed. Therefore, the second residual noise information e2 is filtered to obtain the third residual noise information e3, where e3 = dn - y' - mc'. The third residual noise information e3 and the music information mc are calculated using the LMS algorithm to update the weight coefficients of the first filter.

[0080] 110. Based on the SPSA algorithm, the noise processing device is updated using third residual noise information.

[0081] In this embodiment, after the third residual noise information e3 is filtered by the first filter, the music information mc in it approaches zero. Finally, the second filter is updated using the third residual noise information through the SPSA algorithm, which is the same as the expression in step 106 above.

[0082] In this embodiment, by determining whether the music mode is enabled, the received noise information is filtered accordingly, and the sound used to cancel the external noise information is played to obtain residual noise information. The residual noise information is directly fed into the SPSA algorithm for calculation to update the weight coefficients of the second filter. If the music mode is enabled, the residual noise information also contains music information, so the music information needs to be removed separately by the first filter. This achieves the ability to cancel external noise and improve the noise reduction effect in music mode, call mode, or other modes.

[0083] The active noise reduction method in the embodiments of the present invention has been described above. The active noise reduction device in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the active noise cancellation device in this invention includes:

[0084] The acquisition module 201 is used to acquire external noise and external noise information obtained after the external noise passes through the acoustic path;

[0085] Processing module 202 is used to process external noise according to the preset noise processing device to obtain noise processing information;

[0086] The monitoring module 203 is used to determine whether the preset music mode is enabled;

[0087] The first judgment module 204 is used to play noise processing information if it is not enabled.

[0088] The first cancellation module 205 is used to cancel the mixed information with the external noise information to obtain the second residual noise information, wherein the music information is included in the second residual noise information;

[0089] The first update module 206 is used to update the noise processing device based on the SPSA algorithm and using the first residual noise information.

[0090] The second judgment module 207 is used to play mixed information if it is enabled, wherein the mixed information includes noise processing information and music information;

[0091] The second cancellation module 208 is used to cancel the noise processing information with the external noise information to obtain the second residual noise information;

[0092] Filtering module 209 is used to filter the music information in the second residual noise information according to the preset first filter to obtain the third residual noise information;

[0093] The second update module 210 is used to update the noise processing device based on the SPSA algorithm and utilizing the third residual noise information.

[0094] In another embodiment of the active noise cancellation device described in this invention, the active noise cancellation device includes:

[0095] The acquisition module 201 is used to acquire external noise and external noise information obtained after the external noise passes through the acoustic path;

[0096] Processing module 202 is used to process external noise according to the preset noise processing device to obtain noise processing information;

[0097] The monitoring module 203 is used to determine whether the preset music mode is enabled;

[0098] The first judgment module 204 is used to play noise processing information if it is not enabled.

[0099] The first cancellation module 205 is used to cancel the mixed information with the external noise information to obtain the second residual noise information, wherein the music information is included in the second residual noise information;

[0100] The first update module 206 is used to update the noise processing device based on the SPSA algorithm and using the first residual noise information.

[0101] The second judgment module 207 is used to play mixed information if it is enabled, wherein the mixed information includes noise processing information and music information;

[0102] The second cancellation module 208 is used to cancel the noise processing information with the external noise information to obtain the second residual noise information;

[0103] Filtering module 209 is used to filter the music information in the second residual noise information according to the preset first filter to obtain the third residual noise information;

[0104] The second update module 210 is used to update the noise processing device based on the SPSA algorithm and utilizing the third residual noise information.

[0105] Specifically, the processing module 202 is used for:

[0106] Convolve the external noise with the synchronous perturbation random vector to obtain the convolution information;

[0107] The synchronous perturbation random vector needs to follow a Bernoulli distribution Δ k,in,

[0108] The received external noise is filtered by the second filter, and corresponding filtering information is generated.

[0109] The convolutional and filtering information are superimposed to obtain noise processing information.

[0110] Specifically, the first update module 206 is used for:

[0111] Using the first residual noise information as the cost function, and substituting the cost function into the gradient estimation calculation, the gradient Δ is obtained. w (n);

[0112] Furthermore, the specific expression for "using the first residual noise information as the cost function" is as follows:

[0113] Where λ = 1, 2, 3...n, e (n) J(u(n)) represents the first residual noise information, and J(u(n)) represents the estimated value of the cost function.

[0114] Furthermore, "substitute the cost function into the gradient estimation calculation to obtain the gradient Δ". w The specific expression for (n) is: Among them, c k This is the gain coefficient of the synchronization disturbance vector.

[0115] Gradient Δ w Substituting (n) into the coefficient update equation, the weight coefficients of the second filter are updated. The specific coefficient update equation is W. (n+1) =W (n) -a k *Δ w (n), where W (n) Let a be the current weight coefficients of the second filter. k The step size.

[0116] The above is attached Figure 4 The active noise cancellation device in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The active noise cancellation device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0117] Appendix Figure 5This is a schematic diagram of an active noise cancellation device 300 provided in an embodiment of the present invention. The active noise cancellation device 300 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the active noise cancellation device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the active noise cancellation device 300.

[0118] The active noise cancellation device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 4 The active noise cancellation device structure shown does not constitute a limitation on active noise cancellation devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0119] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the active noise reduction method and system described above.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0121] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An active noise reduction method, characterized in that, include: Acquire external noise and external noise information obtained after the external noise passes through an acoustic path; The external noise is processed according to the preset noise processing device to obtain noise processing information; Determine if the preset music mode is enabled; If not enabled, the noise processing information will be played. The first residual noise information is obtained by canceling out the external noise information with the noise processing information; Based on the SPSA algorithm, the weight coefficients of the noise processing device are updated using the first residual noise information; If enabled, mixed information is played, including the noise processing information and music information; The second residual noise information is obtained by canceling out the mixed information with the external noise information, wherein the music information is included in the second residual noise information; According to the preset first filter, the music information in the second residual noise information is filtered to obtain the third residual noise information; Based on the SPSA algorithm, the weight coefficients of the noise processing device are updated using the third residual noise information; The first filter is specifically designed to separate music information from residual noise information containing music information, so as to avoid music information interfering with the weight coefficient update of the noise processing device.

2. The active noise reduction method according to claim 1, characterized in that, The noise processing device includes a random generator for generating a random vector of synchronous perturbation and a second filter.

3. The active noise reduction method according to claim 2, characterized in that, The step of processing the external noise according to the preset noise processing device to obtain noise processing information includes: The external noise is convolved with the synchronous perturbation random vector to obtain convolution information; The received external noise is filtered according to the second filter, and corresponding filtering information is generated; The convolutional information and the filtering information are superimposed to obtain noise processing information.

4. The active noise reduction method according to claim 3, characterized in that, The synchronous perturbation random vector Following the Bernoulli distribution, where, .

5. The active noise reduction method according to claim 4, characterized in that, The step of updating the weight coefficients of the noise processing device using the first residual noise information based on the SPSA algorithm includes: The first residual noise information is used as the cost function, and the cost function is substituted into the gradient estimation calculation to obtain the gradient. ; The gradient Substituting these values ​​into the coefficient update equation, the weight coefficients of the second filter are updated. The specific coefficient update equation is as follows: , wherein The current weight coefficients of the second filter, the The step size.

6. The active noise reduction method according to claim 5, characterized in that, The step of using the first residual noise information as a cost function includes: The specific expression for the cost function is as follows: ,in, =1, 2, 3...n, the aforementioned For the first residual noise information, the This is an estimate of the cost function.

7. The active noise reduction method according to claim 6, characterized in that, The cost function is substituted into the gradient estimation calculation to obtain the gradient. include: The specific expression for calculating the gradient estimation is as follows: , wherein This is the gain coefficient of the synchronization disturbance vector.

8. An active noise cancellation device, characterized in that, The active noise cancellation device includes: The acquisition module is used to acquire external noise and external noise information obtained after the external noise passes through an acoustic path; The processing module is used to process the external noise according to the preset noise processing device to obtain noise processing information; The monitoring module is used to determine whether the preset music mode is enabled; The first judgment module is used to play the noise processing information if it is not enabled. The first cancellation module is used to cancel the external noise information with the noise processing information to obtain the first residual noise information; The first update module is used to update the noise processing device based on the SPSA algorithm and utilizing the first residual noise information. The second judgment module is used to play mixed information if it is enabled, wherein the mixed information includes the noise processing information and the music information; The second cancellation module is used to cancel the mixed information with the external noise information to obtain second residual noise information, wherein the music information is included in the second residual noise information; The filtering module is used to filter the music information in the second residual noise information according to the preset first filter to obtain the third residual noise information; The second update module is used to update the weight coefficients of the noise processing device based on the SPSA algorithm and utilizing the third residual noise information. The first filter is specifically designed to separate music information from residual noise information containing music information, so as to avoid music information interfering with the weight coefficient update of the noise processing device.

9. An active noise cancellation device, characterized in that, The active noise cancellation device includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor invokes the instructions in the memory to cause the active noise cancellation device to perform the active noise cancellation method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the active noise reduction method as described in any one of claims 1-7.

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