An active noise control method and apparatus

Abstract

The application discloses an active noise control method applied to control of wideband and narrowband mixed noise, which comprises the following steps: identifying acquired primary noise, wherein the primary noise comprises wideband noise and narrowband noise; acquiring a secondary channel of active noise control, wherein the secondary channel comprises a time-domain secondary channel and a frequency channel of narrowband noise; determining a robust wideband filter based on the time-domain secondary channel and the primary noise signal through a Wiener filtering algorithm; and performing active noise control on the primary noise through a plurality of robust wideband filters and an adaptive narrowband filter. The method can efficiently reduce wideband and narrowband mixed noise and realize active noise control.

Description

Technical Field

[0001] This invention relates to the field of noise control technology, and in particular to an active noise control method and apparatus. Background Technology

[0002] Active noise control (ANC) is based on the principle of superposition of sound waves, generating anti-noise to cancel primary noise. Specifically, anti-noise signals with the same amplitude but opposite phase are generated and combined with unwanted noise, causing the two noise signals to cancel each other out. ANC has excellent low-frequency noise suppression capabilities. Due to its efficient reduction of low-frequency noise, ANC has a wide range of applications. Real noise typically contains both broadband components and energy-concentrated line spectra, such as wind turbine noise. Classic broadband ANC control systems based on the filtered-X least mean square (FXLMS) algorithm can be used to control mixed noise containing both broadband and narrowband components. However, research shows that when the noise energy is relatively concentrated, the eigenvalue diffusion of the reference signal's autocorrelation matrix is ​​large. Therefore, broadband ANC control systems suffer from slow convergence when controlling mixed noise. Furthermore, when the secondary channel is a non-minimum phase system or modeling errors exist, the noise reduction performance of broadband ANC control systems deteriorates significantly. Narrowband active noise control systems based on the FXLMS algorithm have advantages such as fast convergence speed and good steady-state noise reduction performance. However, due to the limitation of the internal sinusoidal reference signal, narrowband active noise control systems can only reduce noise at specific frequencies. Therefore, a new noise control method is urgently needed to control mixed noise. Summary of the Invention

[0003] To address the problems existing in the prior art, embodiments of this application provide an active noise control method, apparatus, computing device, computer storage medium, and product containing a computer program, which can effectively control mixed broadband and narrowband noise.

[0004] In a first aspect, embodiments of this application provide an active noise control method, characterized in that it is applied to the control of mixed broadband and narrowband noise. The method includes: identifying acquired primary noise, wherein the primary noise includes broadband noise and narrowband noise; acquiring a secondary channel for active noise control, the secondary channel including a time-domain secondary channel and a frequency channel of the narrowband noise; determining a robust broadband filter based on the time-domain secondary channel and the primary noise signal using a Wiener filtering algorithm; and performing active noise control on the primary noise using multiple robust broadband filters and a narrowband filter.

[0005] In some possible implementations, identifying the acquired primary noise includes: when the primary sound source can produce sound normally, using a reference microphone to collect the primary noise generated by the primary sound source, performing a Fourier transform on the primary noise to obtain the frequency domain signal of the number of channels of the reference microphone; and acquiring the frequency of the narrowband noise in the frequency domain signal.

[0006] In some possible implementations, the frequency channel of the narrowband noise is obtained by: acquiring the time-domain signal of the Gaussian white noise output by the secondary sound source through an error microphone; filtering the time-domain secondary channel through Wiener; and determining the narrowband frequency channel based on the time-domain secondary channel.

[0007] In some possible implementations, the time-domain secondary channels are calculated using Wiener filtering, including: letting the m-th of M secondary sound sources output Gaussian white noise; using L error microphones to acquire the current noise signal; and using Wiener filtering to calculate the secondary channels from the m-th secondary sound source to each error microphone based on the Gaussian white noise and the current noise signal.

[0008] In some possible implementations, determining the narrowband frequency channel based on the time-domain secondary channel includes: convolving the sine wave corresponding to the narrowband noise frequency with the time-domain secondary channel, canceling it out using an adaptive discrete Fourier series analyzer, and determining the narrowband frequency channel based on the steady-state value of the adaptive discrete Fourier series analyzer.

[0009] In some possible implementations, the robust broadband filter is determined by the Wiener filtering algorithm by: acquiring the primary noise collected by the reference microphone and the time-domain signal of the error microphone when the secondary sound source is emitting sound; and determining the robust broadband filter based on the primary noise, the desired signal, and the time-domain secondary channel.

[0010] In some possible implementations, primary noise is actively controlled by multiple robust broadband and narrowband filters, including: acquiring a reference signal through a reference microphone, convolving the reference signal with a robust broadband filter to determine broadband secondary noise; convolving a narrowband reference signal with a narrowband filter to determine narrowband secondary noise; summing the broadband secondary noise and the narrowband secondary noise, and outputting the summation result through a secondary sound source.

[0011] In some possible implementations, based on Gaussian white noise and the current noise signal, Wiener filtering is used to calculate the secondary channels from the m-th secondary sound source to each error microphone, according to the following formula.

[0012]

[0013] In the formula, s lm =[s lm0 , ..., slm(J-1) ] T J represents the order of the secondary channel, R yy Characterizing Gaussian white noise y m，white The autocorrelation matrix of (n), p yd Characterizing the Gaussian white noise signal and the noise d acquired by the current error microphone l，white The cross-correlation vector of (n), and

[0014]

[0015] p yd =E[d l，white (n)y m，white (n)]

[0016] In the formula, E represents the mathematical expectation, T represents the transpose of the matrix, d represents the noise signal collected by the current error microphone, and n represents the sampling point index.

[0017] In some possible implementations, the narrowband frequency channel includes a real part and an imaginary part, denoted as .

[0018] S re，qlm (n+1)=S re，qlm (n)+μ k cos(2πf q n / F s )e(n),

[0019] S im，qlm (n+1)=S im，qlm (n)-μ k sin(2πf q n / F s )e(n),

[0020] In the formula, S re，qlm Characterizing the real part, S im，qlm The imaginary part is represented by n, and the sampling point index is represented by μ. k Characterizes an adaptive step size, f q The frequency F characterizing narrowband noise s The sampling rate is represented by , and e represents the error of the adaptive discrete Fourier series analyzer.

[0021] Secondly, embodiments of this application provide an active noise control device, characterized in that it is applied to the control of mixed broadband and narrowband noise. The device includes: an identification module for identifying acquired primary noise, wherein the primary noise includes broadband noise and narrowband noise; an acquisition module for acquiring a secondary channel for active noise control, wherein the secondary channel includes a time-domain secondary channel and a frequency channel of the narrowband noise; a processing module for determining a robust broadband filter based on the time-domain secondary channel and the primary noise signal using a Wiener filtering algorithm; and an output module for performing active noise control on the primary noise using multiple robust broadband filters and an adaptive narrowband filter.

[0022] Thirdly, embodiments of this application provide a computer-readable storage medium including computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method as described in any of the first aspects.

[0023] Fourthly, embodiments of this application provide a computing device, including a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, perform the method as described in any of the first aspects.

[0024] Fifthly, embodiments of this application provide a product comprising a computer program that, when the computer program product is run on a processor, causes the processor to perform the method as described in any of the first aspects.

[0025] The solution provided in this application embodiment can reduce narrowband noise more efficiently and achieve active noise control. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of an active noise control method provided in an embodiment of this application;

[0028] Figure 2 This is a schematic diagram of the overall process of active noise control provided in an embodiment of this application;

[0029] Figure 3 These are schematic diagrams illustrating the effects of different methods for active noise control provided in the embodiments of this application;

[0030] Figure 4This is a schematic diagram of the structure of an active noise control device provided in an embodiment of this application;

[0031] Figure 5 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] In this article, the term "and / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone.

[0034] In this article, the symbol " / " indicates that the related objects are in an "or" relationship. For example, A / B means A or B.

[0035] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0036] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0038] In the description of the embodiments of this application, unless otherwise stated, noise and noise signal refer to the same concept. For example, primary noise and primary noise signal both refer to noise signals.

[0039] To facilitate understanding of the embodiments of this application, the following will provide further explanation and description with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0040] First, let me introduce the technical terms used in this application:

[0041] 1. Primary sound source: A noise field that is to be controlled is called a primary sound field, and its source is called a primary sound source. The noise produced by the primary sound source is primary noise or primary sound wave.

[0042] 2. Secondary sound source: Noise generated by humans to cancel primary noise is called secondary noise or secondary sound wave. The actuator that generates secondary noise is called secondary actuator. If the actuator is a sound source, it is called secondary sound source.

[0043] 3. Reference microphone, a microphone used to collect primary noise.

[0044] 4. Error microphone, a microphone used to collect the error signal after primary and secondary noise cancellation.

[0045] 5. Secondary channel: The equivalent filter from the secondary sound source to the error microphone. Convolving the secondary noise with the secondary channel yields the corresponding acoustic signal at the error microphone where the secondary noise propagates.

[0046] Next, the technical solutions provided in the embodiments of this application will be introduced.

[0047] In active noise control, a feedforward hybrid active noise control system has been proposed to effectively control mixed wide and narrow band noise. This system includes a sinusoidal noise canceller and two adaptive subsystems. These two subsystems are parallel wideband and narrowband subsystems, used to control the wideband and narrowband components of the mixed noise, respectively. Furthermore, to reduce interference between the wideband and narrowband controllers, the feedforward hybrid active noise control system also includes a sinusoidal noise canceller to remove line spectra from the wideband reference signal; this is typically a discrete Fourier analyzer or a second-order infinite impulse response (IIR) notch filter. Linear prediction is also used to separate the narrowband component in the reference signal. Simulations and experiments show that the feedforward hybrid active noise control system has excellent control performance for mixed wide and narrow band noise.

[0048] However, feedforward hybrid active noise control systems still have some problems. First, the parallel adaptive controllers included in the feedforward hybrid active noise control system can lead to mutual coupling. This problem can be improved by adding a residual noise separator. Alternatively, the estimated primary channel can be used to separate the error signal to achieve decoupling. However, while both methods can reduce the mutual interference between parallel controllers to some extent, they increase the computational burden. Second, the sinusoidal noise canceller that generates the broadband reference signal can severely affect the system performance. A wide bandwidth of the sinusoidal noise canceller reduces the broadband noise reduction effect near the narrowband noise frequency. A narrow bandwidth of the sinusoidal noise canceller slows down the convergence speed of the controller and makes it more sensitive to frequency mismatch. An unsuitable sinusoidal noise canceller can lead to a degraded performance of the feedforward hybrid active noise control system, even worse than a traditional broadband active noise control system.

[0049] In view of this, this application proposes a noise control method, which involves acquiring primary noise through a reference microphone, identifying the time-domain secondary channel from the secondary loudspeaker to the error microphone, extracting a narrowband frequency-domain secondary channel through the time-domain secondary channel, calculating a robust broadband filter through the time-domain secondary channel and the primary noise signal, and then using a parallel robust broadband filter and an adaptive narrowband filter for active noise control.

[0050] For example, Figure 1 A schematic diagram of a noise control method provided in an embodiment of this application is shown. This method can be applied to the control of mixed broadband and narrowband noise. Figure 1 As shown, the noise control method may include the following steps:

[0051] S11: Identify the acquired primary noise, which includes both broadband and narrowband noise.

[0052] In this embodiment, a reference microphone can be used to collect primary noise, and a Fourier transform can be performed on the collected primary noise to identify the narrowband noise contained therein in the transform result.

[0053] Specifically, K (K>1) reference microphones can be used. With the primary sound source capable of producing sound normally, N sampling points of the noise emitted by the primary sound source (i.e., the noise source) are sampled to obtain the time-domain signal of the primary noise. The sampled primary noise is denoted as x. k (n). Where 1≤k≤K, k represents the number of reference microphones, n represents the sampling point index, and satisfies 1≤n≤N, where N represents the number of sampling points. The noise collected by each microphone is one channel. The primary noise x of the K channels collected by the reference microphones is... k (n) Perform a Fourier transform to obtain the frequency domain X corresponding to the K channels.k (f) Signal. Where f represents the frequency index of the frequency domain signal, and satisfies 1≤f≤F s / 2, F s Characterize the sampling rate. Convert the frequency domain X of the K channels... k (f) The frequency of prominent narrowband noise in the signal is denoted as f. q Where q represents the narrowband noise frequency index, and satisfies 1≤q≤Q, and Q represents the total number of narrowband noise frequencies in the primary noise.

[0054] S12: Obtain the secondary channel for active noise control, the secondary channel including a time-domain secondary channel and a frequency channel for narrowband noise.

[0055] In this embodiment, M (M>0) secondary sound sources (e.g., secondary loudspeakers) can be used to output white noise, and the time-domain signal at this time is acquired using an error microphone. Next, the time-domain secondary channel is calculated using Wiener filtering. Based on the time-domain secondary channel, the narrowband frequency channel is determined.

[0056] In some possible embodiments, computing the time-domain secondary channel includes: letting the m-th of M secondary sound sources output Gaussian white noise, denoted as y. m，white (n), where m represents the index number of the secondary sound source (i.e., the m-th secondary sound source), and satisfies 1≤m≤M, where M represents the total number of secondary sound sources. The noise at this time is collected using L error microphones, and this noise is denoted as d. l，white (n), where l represents the index number of the error microphone, and satisfies 1≤l≤L, and L represents the total number of error microphones. Based on Gaussian white noise signal y m，white (n) and noise signal d l，white (n), the secondary channels from the m-th secondary sound source to each error microphone are calculated using the Wiener filtering method. Let the value of m vary from 1 to M to obtain the time-domain secondary channels from the M secondary sound sources to the L error microphones. This time-domain secondary channel is denoted as s. lm .

[0057] In some possible embodiments, based on the Gaussian white noise signal y m，white (n) and noise signal d l，white (n), using the Wiener filtering method to calculate the secondary channels from the m-th secondary sound source to each error microphone, can be calculated according to the following formula.

[0058]

[0059] In the formula, s lm =[s lm0 , ..., s lm(J-1) ] T J represents the order of the secondary channel, Ryy Characterizing Gaussian white noise y m，white The autocorrelation matrix of (n), p yd Characterizing the Gaussian white noise signal and the noise d collected by the error microphone at this time l，white The cross-correlation vector of (n), and

[0060]

[0061] p yd =E[d l，white (n)y m，white (n)]

[0062] In the formula, E represents the mathematical expectation, T represents the transpose of the matrix, d represents the noise signal collected by the current error microphone, and n represents the sampling point index.

[0063] In some possible embodiments, determining the narrowband frequency channel based on the time-domain secondary channel includes: convolving the sine wave corresponding to the narrowband noise frequency with the time-domain secondary channel, canceling it out using an adaptive discrete Fourier series analyzer, and determining the narrowband frequency channel based on the steady-state value of the adaptive discrete Fourier series analyzer.

[0064] In this embodiment, for the time-domain secondary channel s from the m-th secondary sound source to the l-th error microphone... lm The analog error signal is obtained by convolving the first reference signal with the time-domain secondary channel and subtracting the result from the output of the discrete Fourier analyzer. The first reference signal can be a sinusoidal signal, represented as...

[0065] sin(2πf q n / F s )

[0066] In the formula, f q The frequency characterizing narrowband noise, n characterizing the sampling point index, F s Characterizes the sampling rate.

[0067] Using the first and second signals, the narrowband frequency f is extracted through an adaptive discrete Fourier series matching method. q The real and imaginary parts of the secondary channel in the frequency domain are given, with the real part denoted as S. re，qlm The imaginary part is denoted as S. im，qlm The second reference signal can be a cosine signal, represented as:

[0068] cos(2πf q n / F s )

[0069] In the formula, f q The frequency characterizing narrowband noise, n characterizing the sampling point index, F sCharacterizes the sampling rate.

[0070] Let q vary from 1 to Q, m vary from 1 to M, and l vary from 1 to L. Calculate the real part S of the secondary channels from all M secondary sound sources to L error microphones in the narrowband frequency domain at all Q frequencies. re，qlm And the imaginary part S im，qlm .

[0071] In some possible embodiments, the narrowband frequency f is extracted using an adaptive discrete Fourier series matching method. q The real and imaginary parts of the secondary channel in the frequency domain can be obtained using the following formula.

[0072] S re，qlm (n+1)=S re，qlm (n)+μ k cos(2πf q n / F s )e(n),

[0073] S im，qlm (n+1)=S im，qlm (n)-μ k sin(2πf q n / F s )e(n),

[0074] In the formula, n represents the sampling point index, and μ k The step size represents an adaptive step size, and e represents the error of the adaptive discrete Fourier series analyzer.

[0075] S13: Based on the time-domain secondary channel and primary noise signal, a robust broadband filter is determined using the Wiener filtering algorithm.

[0076] In this embodiment, when the secondary sound source emits sound, the time-domain signals of K reference microphones and L error microphones can be collected, denoted as d. l (n). Primary noise x from K channels acquired via the reference microphone. k (n), time-domain signal d l (n), and all time-domain secondary channels S m，l Determine all broadband filters. Let the broadband filter be denoted as w. mk Then w mk =[w mk0 , ..., w mk(I-1) ] T , where I represents the order of the broadband filter and T represents the transpose of the matrix.

[0077] In some possible embodiments, determining all broadband filters includes: integrating all broadband filters into a coefficient vector, denoted as... Among them, the i-th broadband filter

[0078] w i =[w 11i w 12i , ..., w 1Ki w 21i , ..., w MKi ] T .

[0079] Define a matrix consisting of wideband filtered reference signal vectors, denoted as R(n), and expressed as follows:

[0080]

[0081] Where, r l (n)=[r l11 (n), r l12 (n), ..., r t1K (n), r l21 (n), ..., r lMK (n)] T Filtered reference signal r lmk (n) can be represented as

[0082]

[0083] The calculation method for the robust broadband controller can then be obtained as follows:

[0084] w=-{E[R T [n]R(n)+λG]} -1 E[R T [(n)d(n)],

[0085] In the formula, E represents the mathematical expectation, λ represents the robustness factor, is a constant, G represents the identity matrix, and d(n) represents the primary noise vector, and d(n) = [d1(n), ..., dn(n)]. L (n)] T .

[0086] S14: Active noise control is achieved through multiple robust broadband and narrowband filters.

[0087] In this embodiment, the reference signal acquired by the reference microphone is convolved with a robust broadband filter to obtain broadband secondary noise. The internally generated narrowband reference signal is convolved with an adaptive narrowband filter to obtain narrowband secondary noise. The broadband secondary noise and the narrowband secondary noise are summed and output through a secondary sound source to achieve active noise control.

[0088] Specifically, when the primary sound source emits sound, a reference microphone can be used to collect the real-time signal x. k(n), together with the first reference signal and the second reference signal, calculate the output signals of the M secondary sound sources, expressed as:

[0089]

[0090] In the formula, w aqm (n) and w bqm (n) characterizes the narrowband frequency f q The narrowband filter coefficients of the corresponding m-th secondary sound source.

[0091] Next, the error signal e can be acquired using L error microphones. l (n), the narrowband filter coefficients are updated using the FXLMS algorithm. After determining the narrowband filter coefficients, the broadband secondary noise and the narrowband secondary noise are summed, and the summation result is output through the secondary sound source.

[0092] In some possible embodiments, updating the narrowband filter coefficients includes: defining the narrowband filter coefficient vector as...

[0093]

[0094]

[0095] in,

[0096] w aq (n)=[w aq1 (n), ..., w aqM (n)] T ,

[0097] w bq (n)=[w bq1 (n), ..., w bqM (n)] T .

[0098] Given that single-frequency signals have frequency sampling characteristics, the narrowband reference signal can be expressed as:

[0099]

[0100]

[0101] Narrowband filter reference signals can be composed of a matrix

[0102] R a (n)=[R a1 (n), ..., R aQ (n)],

[0103] R b (n)=[R b1 (n), ..., RbQ (n)],

[0104] in,

[0105]

[0106]

[0107] The specific steps for updating the narrowband filter coefficients using the FXLMS algorithm are as follows:

[0108]

[0109]

[0110] Where μ represents the vector composed of narrowband step sizes, and has e(n) represents the error signal vector, and e(n) = [e1(n), ..., e L (n)] T .

[0111] The above describes the active noise control method provided in this application. It involves acquiring primary noise through a reference microphone, identifying the time-domain secondary channel from the secondary speaker to the error microphone, extracting a narrowband frequency-domain secondary channel from the time-domain secondary channel, calculating a robust broadband filter using the time-domain secondary channel and the primary noise signal, and then using a parallel robust broadband filter and an adaptive narrowband filter for active noise control. In this method, the broadband portion uses a fixed-coefficient filter, ensuring that the convergence of the narrowband controller is not interfered with. Therefore, the narrowband subsystem can more efficiently reduce narrowband noise. Furthermore, this method does not require removing the narrowband component from the broadband reference signal, so the control effect remains unaffected. The computational load required by the fixed broadband controller is far less than that of the adaptive filter, making it easier to apply to multi-channel systems.

[0112] For example, Figure 2 A schematic diagram illustrating a specific method for active noise control provided in an embodiment of this application is shown. Figure 2 As shown, the primary noise is processed by a robust broadband filter to obtain broadband secondary noise y. b (n), that is, in step S14 of the aforementioned method, the relevant formula y m (n) Partially, based on the real-time noise signal acquired by the reference microphone, a first reference signal and a second reference signal are generated. These two reference signals are then used to calculate the narrowband filter coefficients. The summation of the calculation results yields the narrowband secondary noise y. q (n), that is, in step S14 of the aforementioned method, the relevant formula y m (n) Partially, for each value of q from 1 to Q, the narrowband secondary noise and wideband secondary noise are summed to obtain the summation result y(n). This summation result is output through the secondary sound source and passed through the secondary channel to obtain the noise cancellation signal. Summing the noise cancellation signal with the primary noise signal yields the noise-controlled result. However, there may be errors when summing the noise cancellation signal and the primary noise signal. Therefore, the generated error signal can be used to update the two narrowband filter coefficients to make the narrowband secondary noise more accurate. When updating the narrowband filter coefficients, the first reference signal and the second reference signal can be processed through the narrowband secondary frequency domain channel, combined with the error signal, and updated using the LMS algorithm.

[0113] It is understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. Furthermore, in some possible implementations, each step in the above embodiments may be selectively executed according to actual circumstances; it may be partially or fully executed, without limitation here. All or part of any feature of any embodiment of this application can be freely and arbitrarily combined without contradiction. The combined technical solutions are also within the scope of this application.

[0114] For example, Figure 3 The diagram illustrates the effects of different methods for active noise control. For example... Figure 3 As shown, the dark solid line represents the original, uncontrolled noise, the light solid line represents the noise control method after broadband filtering, and the other two dashed lines represent the noise control results achieved using the method described in this application. One line shows the noise signal with a narrowband step size of 0.001, and the other line shows the noise signal with a narrowband step size of 0.003. Figure 3 The noise reduction effect shown, achieved by the method provided in this application embodiment, is better than using only broadband filtering in terms of noise control. Increasing the narrowband step size can improve the narrowband noise reduction performance of the proposed algorithm, but it will slightly enhance broadband noise near the narrowband frequency. In practical applications, an appropriate narrowband step size needs to be selected according to the requirements.

[0115] Compared to classic fixed-coefficient broadband active noise control algorithms, the proposed method exhibits superior narrowband noise reduction performance. The noise reduction performance of broadband active noise control algorithms using Wiener filters is affected by the secondary channel. Since finite impulse response (IIR) filters cannot perfectly model non-minimum-phase systems, the Wiener filter cannot completely eliminate primary noise when the secondary channel is a non-minimum-phase system. Furthermore, when the secondary channel model contains errors, there is a deviation between the calculated Wiener filter and the actual optimal filter. Such deviations can lead to a decrease in noise reduction effectiveness. On the other hand, narrowband active noise control algorithms have been proven to always perfectly eliminate single-frequency noise. Therefore, narrowband active noise control algorithm systems have a significant advantage in controlling the narrowband component in mixed noise. The simplified feedforward hybrid active noise control system proposed in this application improves the overall narrowband noise reduction performance by arranging a narrowband active noise control subsystem.

[0116] Furthermore, compared to feedforward hybrid active noise control systems, the method proposed in this application eliminates the sinusoidal noise canceller to avoid adversely affecting the system's noise reduction effect. The method provided in this application reduces the notch filter bandwidth parameters that need adjustment, improving system stability and practicality. Based on this, to remove the coupling between the broadband and narrowband controllers, this application uses a fixed-coefficient broadband filter instead of an adaptive broadband filter. Therefore, the narrowband controller can operate relatively independently. Under stable primary noise conditions, the method provided in this application greatly simplifies the feedforward hybrid active noise control system while ensuring noise reduction performance. Moreover, the use of a fixed-coefficient broadband controller in the method proposed in this application results in a significantly lower computational load compared to the feedforward hybrid active noise control system, especially in multi-channel scenarios.

[0117] Based on the methods in the above embodiments, this application also provides an active noise control device.

[0118] For example, Figure 4 An active noise control device is shown, which can be deployed on a computing device for controlling mixed broadband and narrowband noise. Figure 4 As shown, the device includes an identification module 401, an acquisition module 402, a processing module 403, and an output module 404.

[0119] The identification module 401 is used to identify narrowband noise in the acquired primary noise, wherein the primary noise includes broadband noise and the narrowband noise.

[0120] Acquisition module 402 is used to acquire the frequency channel of the narrowband noise, the frequency channel including a time-domain secondary channel;

[0121] Processing module 403 is used to determine a robust broadband filter based on the time-domain secondary channel and primary noise signal using the Wiener filtering algorithm;

[0122] The output module 404 is used to perform active noise control on the primary noise through a plurality of robust broadband filters and narrowband filters.

[0123] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0124] Figure 5 This diagram illustrates the structure of a computer device according to an embodiment of this specification. The computer device may include a processor 510, a memory 520, an input / output interface 530, a communication interface 540, and a bus 550. The processor 510, memory 520, input / output interface 530, and communication interface 540 are interconnected internally via the bus 550. This computer device can be used to perform the aforementioned... Figure 2 The method shown.

[0125] The processor 510 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0126] The memory 520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 520 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 520 and is called and executed by the processor 510.

[0127] Input / output interface 530 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0128] The communication interface 540 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0129] The 550 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus 550 is represented by only one line, but this does not mean that there is only one bus or one type of bus. The bus 550 can transfer information between various components of the device (such as processor 510, memory 520, input / output interface 530 and communication interface 540).

[0130] It should be noted that although the above-described device only shows the processor 510, memory 520, input / output interface 530, communication interface 540, and bus 550, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0131] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0132] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0133] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0134] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0135] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0136] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.

Claims

1. An active noise control method, characterized in that, The method, applied to the control of mixed wideband and narrowband noise, includes: Identify the acquired primary noise, which includes both broadband and narrowband noise; A secondary channel for active noise control is obtained, comprising a time-domain secondary channel and a frequency channel for the narrowband noise. Obtaining the frequency channel for the narrowband noise includes: acquiring the time-domain signal of Gaussian white noise output from the secondary sound source using an error microphone; calculating the time-domain secondary channel using Wiener filtering; and determining the narrowband frequency channel based on the time-domain secondary channel, including: convolving the sine wave corresponding to the narrowband noise frequency with the time-domain secondary channel and canceling it using an adaptive discrete Fourier series analyzer; and determining the narrowband frequency channel based on the steady-state value of the adaptive discrete Fourier series analyzer. Based on the time-domain secondary channel and primary noise signal, a robust broadband filter is determined using the Wiener filtering algorithm. Active noise control is performed on the primary noise using multiple robust broadband filters and adaptive narrowband filters.

2. The method according to claim 1, characterized in that, The primary noise acquired through identification includes: When the primary sound source can produce sound normally, the primary noise generated by the primary sound source is collected using a reference microphone, and the primary noise is subjected to a Fourier transform to obtain the frequency domain signal of the number of channels of the reference microphone. Obtain the frequency of narrowband noise in the frequency domain signal.

3. The method according to claim 1, characterized in that, The calculation of the time-domain secondary channel via Wiener filtering includes: Let the m-th of the M secondary sound sources output Gaussian white noise; The current noise signal is acquired using L of the aforementioned error microphones; Based on the Gaussian white noise and the current noise signal, the secondary channels from the m-th secondary sound source to each of the error microphones are calculated using the Wiener filtering method.

4. The method according to claim 1, characterized in that, Robust broadband filters are determined using the Wiener filtering algorithm, including: When the secondary sound source emits sound, the primary noise is collected using a reference microphone, and the desired signal is collected using the error microphone. A robust broadband filter is determined based on the primary noise, the desired signal, and the time-domain secondary channel.

5. The method according to claim 1, characterized in that, The active noise control of the primary noise using multiple robust broadband filters and adaptive narrowband filters includes: A reference signal is acquired by a reference microphone, and the reference signal is convolved with the robust broadband filter to determine the broadband secondary noise. The narrowband reference signal is convolved with the narrowband filter to determine the narrowband secondary noise; The broadband secondary noise and the narrowband secondary noise are summed, and the summation result is output through the secondary sound source.

6. The method according to claim 3, characterized in that, Based on the Gaussian white noise and the current noise signal, the secondary channels from the m-th secondary sound source to each of the error microphones are calculated using Wiener filtering, according to the following formula. In the formula, J represents the order of the secondary channel. Characterizing Gaussian white noise The autocorrelation matrix, Characterizing the Gaussian white noise signal and the noise acquired by the current error microphone The cross-correlation vectors, and In the formula, E represents the mathematical expectation, T represents the transpose of the matrix, d represents the noise signal acquired by the current error microphone, and n represents the sampling point index.

7. The method according to claim 1, characterized in that, The narrowband frequency channel includes a real part and an imaginary part, denoted as . In the formula, Representing the real part, The imaginary part is represented by n, and the index of the sampling point is represented by n. Characterizes an adaptive step size. The frequency characterizing narrowband noise, The sampling rate is represented by , and e represents the error of the adaptive discrete Fourier series analyzer.

8. An active noise control device, characterized in that, The device, used for controlling mixed broadband and narrowband noise, includes: The identification module is used to identify the acquired primary noise, which includes broadband noise and narrowband noise. An acquisition module is used to acquire the secondary channel of active noise control, the secondary channel including a time-domain secondary channel and the frequency channel of the narrowband noise; wherein, acquiring the frequency channel of the narrowband noise includes: acquiring the time-domain signal of Gaussian white noise output by the secondary sound source through an error microphone; calculating the time-domain secondary channel through Wiener filtering; determining the narrowband frequency channel based on the time-domain secondary channel includes: convolving the sine wave corresponding to the frequency of the narrowband noise with the time-domain secondary channel, and canceling it through an adaptive discrete Fourier series analyzer, and determining the narrowband frequency channel according to the steady-state value of the adaptive discrete Fourier series analyzer; The processing module is used to determine a robust broadband filter based on the time-domain secondary channel and the primary noise signal using the Wiener filtering algorithm. The output module is used to perform active noise control on the primary noise through multiple robust broadband filters and adaptive narrowband filters.

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