A robust feedforward hybrid wideband and narrowband active noise control system and method

Abstract

This invention discloses a robust feedforward-type wideband and narrowband hybrid active noise control system and method, belonging to the field of active noise control technology. The system utilizes a first linear predictive filtering subsystem to synthesize narrowband and wideband reference signals, overcoming the frequency offset problem. By employing a second linear predictive filtering subsystem and an auxiliary filtering subsystem, the independence among the wideband secondary sound source synthesis subsystem, the narrowband secondary sound source synthesis subsystem, and the online identification subsystem of the secondary channels is improved, enhancing the accuracy and speed of online identification of the secondary channels and improving the overall system's dynamic performance. Simultaneously, the impact of introduced auxiliary noise on residual noise is reduced, improving the overall system's noise suppression performance. Furthermore, this invention utilizes an online identification module for the secondary channels, which can handle the complex time-varying nature of the secondary channels without requiring the installation of non-acoustic sensors, reducing physical space requirements and hardware costs, and broadening its practical noise reduction application range.

Description

Technical Field

[0001] This invention relates to a robust feedforward hybrid wide and narrow band active noise control system and method, belonging to the field of active noise control technology. Background Technology

[0002] With advancements in electronics, electroacoustics, and signal processing technologies, Active Noise Control (ANC) technology has been widely applied in noise reduction in automobiles, aircraft, and other applications. Compared to traditional passive noise reduction technologies, ANC utilizes the principle of destructive interference of sound waves, exhibiting excellent low-frequency noise suppression performance, as well as advantages such as small size and low cost, making it a valuable complement to traditional passive noise control technologies (L. Lu, K. Yin, RC de Lamare, Z. Zheng, Y. Yu, X. Yang, B. Chen, "A survey on active noise control in the past decade, Part I: Linear systems," Signal Process. 183 (2021), 108039.). Based on the controller's structure, ANC systems can be categorized into three types: feedforward, feedback, and hybrid feedforward / feedback. Further, based on the target noise spectrum characteristics, feedforward ANC systems can be further divided into feedforward narrowband ANC systems, feedforward broadband ANC systems, and feedforward hybrid broadband / narrowband ANC systems.

[0003] Among them, the feedforward hybrid wide-narrowband ANC system can simultaneously reduce noise or interference from both wideband and narrowband noise, effectively solving the "spark" problem that exists in feedforward wideband ANC systems when suppressing mixed wide-narrowband noise (Y. Xiao and J. Wang, "A new feedforward hybrid active noise control system," IEEE Signal Process. Letters, vol. 18, no. 10, pp. 591-594, Oct. 2011.). However, this type of feedforward hybrid wide-narrowband ANC system requires the simultaneous use of non-acoustic and acoustic reference sensors to obtain reference signals. The required non-acoustic reference sensors (such as tachometers) may, due to aging or wear, obtain narrowband reference frequencies that are inconsistent with the actual narrowband component frequencies, i.e., frequency shift occurs. This frequency shift may directly reduce the performance of the narrowband controller in the aforementioned hybrid wide-narrowband ANC system in suppressing the target noise narrowband component, thereby affecting the stability and convergence of the hybrid wide-narrowband ANC system and limiting its practical application. Furthermore, in practical applications, the complex time-varying nature of the secondary channel can also seriously affect the stability of the system. Designing an effective online identification method for the secondary channel of this feedforward wide and narrow band hybrid ANC system has important research and application value, thereby broadening its practical application scope.

[0004] In 2013, Xiao et al. designed a feedforward-type wide- and narrow-band hybrid ANC system to cope with frequency offset. It uses a bandpass filter bank composed of several parallel adaptive notch filters to compensate for frequency offset, thereby providing accurate input to a controller based on amplitude and phase adjustment structure for synthesizing secondary sound sources (Y. Xiao and K. Doi, “A robust hybridactive noise control system using IIR notch filters,” Int. J. Advanced Mechatronic Systems, 5(1), (2013): 69-77.). However, this system has strict requirements on the initial filter weight settings of the adaptive notch filters, and it still has insufficient frequency compensation capability when dealing with large frequency offsets or sudden frequency changes. Recently, Ma & Xiao et al. developed a feedforward hybrid active noise control system based on online secondary-path modelling. This system can cope with the complex time-varying nature of the secondary path, improving the accuracy and speed of online secondary-path modelling while further reducing residual noise (Y.Ma, Y.Xiao, L.Ma, and K.Khorasani, “Arobust feedforward hybrid active noise control system with online secondary-path modelling,” IET Signal Process., 17(1)(2023), e12183.). However, this system does not yet involve frequency offset compensation. Furthermore, although its residual noise separation structure can separate broadband and narrowband noise in the residual noise, it does not effectively separate the broadband components related to the target noise from the broadband components related to the reference signal. This results in the continued coupling between the narrowband controller, the broadband controller, and the online secondary-path modelling module, affecting the convergence performance of the overall system.

[0005] To simultaneously address the performance constraints imposed by frequency offset and the complex time-varying nature of the secondary channel, a more effective and practical feedforward-type wide-narrowband hybrid active noise control system is needed. Summary of the Invention

[0006] To address the problems in traditional feedforward hybrid wide-band and narrow-band ANC systems, such as frequency shifts in non-acoustic reference sensors due to aging and wear, and complex time-varying characteristics of secondary channels, which severely restrict the convergence and stability of feedforward hybrid wide-band and narrow-band ANC systems and thus reduce the overall system's wide-band and narrow-band noise suppression performance, this invention provides a robust feedforward hybrid wide-band and narrow-band active noise control system and method. The technical solution is as follows:

[0007] The first objective of this invention is to provide a robust wideband and narrowband hybrid active noise control system. This active noise control system employs a reference microphone to acquire a reference signal, an error microphone to acquire residual noise, and a secondary loudspeaker to provide a secondary sound source. The actual primary channel in the acoustic space is a channel model of the reference signal propagating to the error microphone; the actual secondary channel in the acoustic space is a channel model of the secondary sound source provided by the secondary loudspeaker propagating to the error microphone. The active noise control system includes a first linear predictive filtering subsystem 1, a wideband secondary sound source synthesis subsystem 2, a narrowband secondary sound source synthesis subsystem 3, a second linear predictive filtering subsystem 4, an auxiliary filtering subsystem 5, and a secondary channel online identification subsystem 6.

[0008] The first linear prediction filtering subsystem 1 is connected to the broadband secondary sound source synthesis subsystem 2, the narrowband secondary sound source synthesis subsystem 3, and the auxiliary filtering subsystem 5, respectively; the broadband secondary sound source synthesis subsystem 2 is connected to the first linear prediction filtering subsystem 1 and the auxiliary filtering subsystem 5, respectively; the narrowband secondary sound source synthesis subsystem 3 is connected to the first linear prediction filtering subsystem 1 and the second linear prediction filtering subsystem 4, respectively; the second linear prediction filtering subsystem 4 is connected to the narrowband secondary sound source synthesis subsystem 3, the auxiliary filtering subsystem 5, and the secondary channel online identification subsystem 6, respectively; the auxiliary filtering subsystem 5 is connected to the first linear prediction filtering subsystem 1, the broadband secondary sound source synthesis subsystem 2, the second linear prediction filtering subsystem 4, and the secondary channel online identification subsystem 6, respectively; the secondary channel online identification subsystem 6 is connected to the second linear prediction filtering subsystem 4 and the auxiliary filtering subsystem 5, respectively.

[0009] The first linear prediction filtering subsystem 1 is used to synthesize a broadband reference signal and a narrowband reference signal; the broadband secondary sound source synthesis subsystem 2 is used to synthesize a broadband secondary sound source; the narrowband secondary sound source synthesis subsystem 3 is used to synthesize a narrowband secondary sound source; the second linear prediction filtering subsystem 4 is used to separate narrowband residual noise components and broadband residual noise components from residual noise; the auxiliary filtering subsystem 5 is used to separate broadband residual noise components related to the broadband reference signal and broadband residual noise components related to auxiliary noise and additive noise in the target signal from the broadband residual noise components; the secondary channel online identification subsystem 6 is used to estimate the time-varying secondary channel model in real time;

[0010] The narrowband residual noise component separated by the second linear prediction filtering subsystem 4 is used as the error output of the narrowband secondary sound source synthesis subsystem 3 and the input of the auxiliary noise adjustment module in the secondary channel online identification subsystem 6, respectively. Simultaneously, the broadband residual noise component related to the broadband reference signal separated by the auxiliary filtering subsystem 5 is used as the error output of the broadband secondary sound source synthesis subsystem 2 and the input of the auxiliary noise adjustment module in the secondary channel online identification subsystem 6, respectively. Furthermore, the broadband residual noise component related to auxiliary noise and additive noise in the target signal separated by the auxiliary filtering subsystem 5 is used as the desired input of the secondary channel online identification subsystem 6. This improves the independence among the broadband secondary sound source synthesis subsystem 2, the narrowband secondary sound source synthesis subsystem 3, and the secondary channel online identification subsystem 6, enhances the accuracy and speed of secondary channel online identification, improves the dynamic performance of the overall system, and reduces the impact of introduced auxiliary noise on residual noise, thereby improving the overall noise suppression performance of the system.

[0011] Optionally, the first linear prediction filter subsystem 1 includes a first delay element 11 and a first linear prediction filter 12, which are connected in series. The order of the first delay element 11 is D1. The coefficients and length of the first linear prediction filter 12 are respectively... The coefficients of L1 and L2 are updated using the least mean square algorithm, and the update formula is as follows:

[0012] h 1,j (n+1)=h 1,j (n)+μ1x w (n)x r (n-D1-j)

[0013] Where μ1 is the update step size of the first linear prediction filter 12, and its value is positive; x w (n) represents the broadband reference signal separated by the first linear prediction subsystem 1, x r (n) is the reference signal provided by the reference microphone; n is the time, n≥0;

[0014] The narrowband reference signal and the wideband reference signal synthesized by the first linear predictive filtering subsystem 1 are as follows:

[0015]

[0016] x w (n)=x r (n)-x f (n)

[0017] Optionally, the broadband secondary sound source synthesis subsystem 2 includes the broadband controller 21 and the first filter-X minimum mean square algorithm module 22;

[0018] The broadband controller 21 employs a linear filter, the coefficients and length of which are respectively... and L w ;

[0019] The first filter-X minimum mean square algorithm module 22 utilizes the broadband reference signal x separated by the auxiliary filter subsystem 5 w (n) Related broadband residual noise component y h (n) is used as the error output and is used to update the coefficients of the broadband controller 21; the coefficient update formula of the broadband controller 21 is:

[0020]

[0021] Where, μ w This is the update step size of the broadband controller 21, and it takes a positive value; For the broadband reference signal x w (n) The output of the secondary channel estimation model in the first filter-X minimum mean square algorithm module 22;

[0022] The broadband secondary sound source obtained by the broadband secondary sound source synthesis subsystem 2 is...

[0023]

[0024] Optionally, the narrowband secondary sound source synthesis subsystem 3 includes the narrowband controller 31 and the second filter-X minimum mean square algorithm module 32;

[0025] The narrowband controller 31 employs a linear filter, the coefficients and length of which are respectively... and L f ;

[0026] The second filter-X minimum mean square algorithm module 32 utilizes the narrowband residual noise component y separated by the second linear prediction filter subsystem 4 LP (n) is used as the error output and is used to update the coefficients of the narrowband controller 31; the coefficient update formula of the narrowband controller 31 is:

[0027]

[0028] Where, μ f This is the update step size of the narrowband controller 31, and it takes a positive value. For the narrowband reference signal x f(n) The output of the secondary channel estimation model in the second filter-X minimum mean square algorithm module 32;

[0029] The narrowband secondary sound source obtained by the narrowband secondary sound source synthesis subsystem 3 is...

[0030]

[0031] Optionally, the second linear prediction filter subsystem 4 includes a second delay element 41 and a second linear prediction filter 42, wherein the second delay element 41 and the second linear prediction filter 42 are connected in series, and the order of the second delay element 41 is D2; the coefficients and length of the second linear prediction filter 42 are respectively... The coefficients of L2 and L3 are updated using the least mean square algorithm, and the update formula is as follows:

[0032] h 2,j (n+1)=h 2,j (n)+μ2e LP (n)e(n-D2-j)

[0033] Where μ2 is the update step size of the second linear prediction filter 42, and its value is positive; e LP e(n) represents the broadband residual noise component separated by the second linear prediction subsystem 4; e(n) represents the residual noise provided by the error microphone.

[0034] The narrowband residual noise component and the broadband residual noise component separated from the residual noise by the second linear predictive filtering subsystem 4 are as follows:

[0035]

[0036] e LP (n)=e(n)-y LP (n)

[0037] The broadband residual noise component e separated by the second linear predictive filtering subsystem 4 LP (n), used as the desired input of the auxiliary filtering subsystem 5.

[0038] Optionally, the auxiliary filtering subsystem 5 includes the linear filter 51 and the least mean square algorithm module 52;

[0039] The coefficients and length of the linear filter 51 are respectively And L3, the coefficients of the linear filter 51 are updated using the least mean square algorithm module 52, and the update formula is:

[0040] h 3,j (n+1)=h 3,j(n)+μ3e h (n)x w (nj)

[0041] Where μ3 is the update step size of the linear filter 51, and its value is positive; e h (n) represents the broadband residual noise component related to the additive noise in the auxiliary noise and the target signal separated by the auxiliary filtering subsystem 5;

[0042] The broadband residual noise component related to the broadband reference signal separated by the auxiliary filtering subsystem 5 is:

[0043]

[0044] The broadband residual noise component e, which is related to the auxiliary noise and additive noise in the target signal, is separated by the auxiliary filtering subsystem 5. h (n), used as the expected input for the secondary channel online identification subsystem 6.

[0045] Optionally, the secondary channel online identification subsystem 6 includes: a secondary channel online identification module 61 and an auxiliary noise adjustment module 62;

[0046] The secondary channel online identification module 61 includes a secondary channel estimation model. The secondary channel online identification module 61 uses e h (n) is the desired input, and the auxiliary noise v0(n) generated by the Gaussian white noise v(n) after passing through the auxiliary noise adjustment module 62 is the reference input. The time-varying secondary channel is estimated in real time using the least mean square algorithm.

[0047] The secondary channel estimation model of the secondary channel online identification module 61 The coefficients and lengths are respectively and The formula for updating the coefficients is:

[0048]

[0049] e s (n)=e h (n)-y s (n)

[0050] Where, μ s This is the update step size for the secondary channel estimation model, and its value is positive; y s (n) is the output of the secondary channel estimation model of the secondary channel online identification module 61;

[0051] The auxiliary noise v0(n) is:

[0052] v0(n)=v(n)G(n)

[0053] G(n) = max{G N (n),G B (n)}

[0054] G N (n)=abs[y LP (n-1)]

[0055] G B (n)=abs[y h (n-1)]

[0056] Wherein, G(n) is the gain adjustment factor of the auxiliary noise adjustment module 62; G N (n) and G B (n) represent y LP (n-1) and y h (n-1) is the output after low-pass filtering; abs[] is for absolute value operation; v(n) is the output with zero mean and variance. Additive white Gaussian noise.

[0057] Optionally, the synthesized secondary sound source is:

[0058] y(n)=y w (n)+y f (n)-v0(n)

[0059] Then y(n) is output to the secondary loudspeaker, where it interferes with and cancels out the target noise in the acoustic space.

[0060] A second objective of this invention is to provide an active noise control method, based on the aforementioned robust feedforward hybrid wideband and narrowband active noise control system, the method comprising:

[0061] Step 1: Set system parameters;

[0062] The following components are configured: a first linear prediction filter 12, a wideband controller 21, a narrowband controller 31, a second linear prediction filter 42, a linear filter 51, and a secondary channel estimation model. The length and step size are set; the order of the first delay element 11 and the first delay element 41 are set respectively; the forgetting factor of the auxiliary noise adjustment module 62 is set; the first linear prediction filter 12, the wideband controller 21, the narrowband controller 31, the second linear prediction filter 42, the linear filter 51, and the secondary channel estimation model are set respectively. The initial values ​​of the gain adjustment factor G(n) of the auxiliary noise adjustment module 62 are both zero; the auxiliary noise v(n) is set.

[0063] Step 2: Synthesize the broadband reference signal and the narrowband reference signal;

[0064] At time n, the reference signal x obtained using the reference microphone r (n), which is separated into a broadband reference signal and a narrowband reference signal after passing through the first linear prediction filtering subsystem 1; the separated broadband reference signal is provided to the broadband secondary sound source synthesis subsystem 2 and the auxiliary filtering subsystem 5 respectively; the separated narrowband reference signal is provided to the narrowband secondary sound source synthesis subsystem 3;

[0065] Step 3: At time n, firstly, the broadband reference signal is synthesized into a broadband secondary sound source y by the broadband secondary sound source synthesis subsystem 2. w (n), the narrowband reference signal is synthesized into a narrowband secondary sound source y by the narrowband secondary sound source synthesis subsystem 3. f (n); secondly, auxiliary noise v0(n) is obtained using auxiliary noise adjustment module 62, and then compared with y w (n) and y f (n) are superimposed to synthesize the secondary sound source y(n); finally, the residual noise e(n) is separated by the second linear prediction filter subsystem 4 to obtain the narrowband residual noise component y. LP (n) and broadband residual noise component e LP (n);

[0066] Step 4: At time n, the broadband residual noise component e LP (n) After passing through the auxiliary filtering subsystem 5, broadband residual noise components e related to the auxiliary noise and additive noise in the target signal are obtained respectively. h (n), and the broadband residual noise component y related to the broadband reference signal. h (n); e h (n) is used as the expected input for the secondary channel online identification module 61;

[0067] Step 5: Update the control system

[0068] According to the reference signal x r (n) and the broadband reference signal x w (n) Calculate the coefficients of the updated first linear prediction filter 12 at time n+1;

[0069] Based on the broadband reference signal x w (n) and y h (n) Calculate the coefficients of the updated broadband controller 21 at time n+1;

[0070] Based on the narrowband reference signal x f (n) and narrowband residual noise y LP (n) Calculate the coefficients of the updated narrowband controller 31 at time n+1;

[0071] Based on the residual noise e(n) and the broadband residual noise component e LP (n) Calculate the coefficients of the updated second linear prediction filter 42 at time n+1;

[0072] Based on the broadband reference signal x w (n) and e h (n) Calculate the coefficients of the updated linear filter 51 at time n+1;

[0073] Based on the auxiliary noise v0(n) and e h (n) Calculate and update the secondary channel estimation model in the secondary channel online identification module 61. The coefficient at time n+1;

[0074] Based on the narrowband residual noise component y LP (n) and y h (n) Calculate the gain adjustment factor of the updated auxiliary noise adjustment module 62 at time n+1;

[0075] Step 6: Return to Step 2 and repeat Steps 2 through 5 until the system converges and reaches a steady state, thus achieving active noise control.

[0076] The beneficial effects of this invention are:

[0077] 1. This invention eliminates the need for non-acoustic sensors, reducing space requirements and system hardware costs;

[0078] 2. This invention utilizes the first linear prediction filtering subsystem to separate the narrowband reference signal, thereby providing accurate input to the narrowband sound source synthesis subsystem and effectively solving the frequency offset problem;

[0079] 3. This invention utilizes the broadband residual noise component related to the broadband reference signal and the broadband residual noise component related to the additive noise in the auxiliary noise and target signal separated by the auxiliary filtering subsystem to reduce the coupling relationship between the broadband secondary sound source synthesis subsystem, the narrowband secondary sound source synthesis subsystem, and the secondary channel online identification subsystem, thereby improving the accuracy and speed of the secondary channel online identification and enhancing the dynamic performance of the overall system.

[0080] 4. This invention utilizes the narrowband residual noise component provided by the second linear prediction filter subsystem and the broadband residual noise component related to the broadband reference signal provided by the auxiliary filter subsystem to jointly adjust the amplitude of the auxiliary noise, which can reduce the influence of the auxiliary noise on the residual noise and improve the noise suppression performance of the overall system. Theoretically, it can make the residual noise of the system tend to the environmental level after it reaches steady state.

[0081] 5. This invention utilizes a secondary channel online identification module to effectively address the complex time-varying nature of the secondary channel in actual situations, thus broadening its practical application scope. Attached Figure Description

[0082] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0083] Figure 1 This is a schematic diagram of a robust feedforward hybrid wide and narrow band active noise control system according to Embodiment 1 of the present invention.

[0084] Figure 2(a) is a dynamic change curve of the mean square residual error in Embodiment 3 of the present invention.

[0085] Figure 2(b) is a dynamic change curve of the mean square error of the secondary channel estimation in Embodiment 3 of the present invention.

[0086] Figure 3 This is a dynamic change curve of the target noise and residual noise in Embodiment 4 of the present invention.

[0087] In the diagram: 1 is the first linear prediction filtering subsystem, 2 is the broadband secondary sound source synthesis subsystem, 3 is the narrowband secondary sound source synthesis subsystem, 4 is the second linear prediction filtering subsystem, 5 is the auxiliary filtering subsystem, and 6 is the secondary channel online identification subsystem; 11 is the first delay element, 12 is the first linear prediction filter, 21 is the broadband controller, 22 is the first filter-X minimum mean square algorithm module, 31 is the narrowband controller, 32 is the second filter-X minimum mean square algorithm module, 41 is the second delay element, 42 is the second linear prediction filter, 51 is the linear filter, 52 is the minimum mean square algorithm module, 61 is the secondary channel online identification module, and 62 is the auxiliary noise adjustment module. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0089] Example 1:

[0090] This embodiment provides a robust feedforward hybrid wide-narrowband active noise control system. (See also...) Figure 1The system schematic shown illustrates that the active noise control system employs a reference microphone to acquire a reference signal, an error microphone to acquire residual noise, and a secondary loudspeaker to provide a secondary sound source. The actual primary channel in the acoustic space is the channel model from the reference signal to the error microphone; the actual secondary channel in the acoustic space is the channel model from the secondary sound source provided by the secondary loudspeaker to the error microphone. The active noise control system includes a first linear predictive filtering subsystem 1, a broadband secondary sound source synthesis subsystem 2, a narrowband secondary sound source synthesis subsystem 3, a second linear predictive filtering subsystem 4, an auxiliary filtering subsystem 5, and a secondary channel online identification subsystem 6.

[0091] The first linear prediction filtering subsystem 1 is connected to the broadband secondary sound source synthesis subsystem 2, the narrowband secondary sound source synthesis subsystem 3, and the auxiliary filtering subsystem 5, respectively; the broadband secondary sound source synthesis subsystem 2 is connected to the first linear prediction filtering subsystem 1 and the auxiliary filtering subsystem 5, respectively; the narrowband secondary sound source synthesis subsystem 3 is connected to the first linear prediction filtering subsystem 1 and the second linear prediction filtering subsystem 4, respectively; the second linear prediction filtering subsystem 4 is connected to the narrowband secondary sound source synthesis subsystem 3, the auxiliary filtering subsystem 5, and the secondary channel online identification subsystem 6, respectively; the auxiliary filtering subsystem 5 is connected to the first linear prediction filtering subsystem 1, the broadband secondary sound source synthesis subsystem 2, the second linear prediction filtering subsystem 4, and the secondary channel online identification subsystem 6, respectively; the secondary channel online identification subsystem 6 is connected to the second linear prediction filtering subsystem 4 and the auxiliary filtering subsystem 5, respectively.

[0092] The first linear predictive filtering subsystem 1 is used to synthesize the broadband reference signal and the narrowband reference signal; the broadband secondary sound source synthesis subsystem 2 is used to synthesize the broadband secondary sound source; the narrowband secondary sound source synthesis subsystem 3 is used to synthesize the narrowband secondary sound source; the second linear predictive filtering subsystem 4 is used to separate the narrowband residual noise component and the broadband residual noise component from the residual noise; the auxiliary filtering subsystem 5 is used to separate the broadband residual noise component related to the broadband reference signal and the broadband residual noise component related to the auxiliary noise and the additive noise in the target signal from the broadband residual noise component; the secondary channel online identification subsystem 6 is used to estimate the time-varying secondary channel model in real time;

[0093] The narrowband residual noise component separated by the second linear prediction filtering subsystem 4 is used as the error output of the narrowband secondary sound source synthesis subsystem 3 and the input of the auxiliary noise adjustment module in the secondary channel online identification subsystem 6, respectively. Simultaneously, the broadband residual noise component related to the broadband reference signal separated by the auxiliary filtering subsystem 5 is used as the error output of the broadband secondary sound source synthesis subsystem 2 and the input of the auxiliary noise adjustment module in the secondary channel online identification subsystem 6, respectively. Furthermore, the broadband residual noise component related to auxiliary noise and additive noise in the target signal separated by the auxiliary filtering subsystem 5 is used as the desired input of the secondary channel online identification subsystem 6. This improves the independence among the broadband secondary sound source synthesis subsystem 2, the narrowband secondary sound source synthesis subsystem 3, and the secondary channel online identification subsystem 6, enhances the accuracy and speed of secondary channel online identification, improves the overall system's dynamic performance, and reduces the impact of introduced auxiliary noise on residual noise, thereby improving the overall system's noise suppression performance.

[0094] The actual primary channel P(z) represents the acoustic space model from the reference microphone to the error microphone; the actual secondary channel S(z) represents the acoustic space model from the secondary loudspeaker to the error microphone.

[0095] The target noise is:

[0096] p(n)=p0(n)+v p (n)

[0097] Where p0(n) is the reference signal x in acoustic space. r (n) The signal propagated to the error microphone via the actual primary channel P(z); v p (n) are groups with a mean of zero and a variance of . Additive white Gaussian noise; n is time, n≥0.

[0098] The first linear predictive filtering subsystem 1 includes a first delay element 11 and a first linear predictive filter 12, which are connected in series. The order of the first delay element 11 is D1. The coefficients and length of the first linear predictive filter 12 are respectively... The coefficients of L1 and L2 are updated using the least mean square algorithm, and the update formula is as follows:

[0099] h 1,j (n+1)=h 1,j (n)+μ1x w (n)x r (n-D1-j)

[0100] Where μ1 is the update step size of the first linear prediction filter 12, and its value is positive; x w(n) is the broadband reference signal separated by the first linear prediction subsystem 1, x r (n) is the reference signal provided by the reference microphone;

[0101] The narrowband and wideband reference signals synthesized by the first linear predictive filtering subsystem 1 are as follows:

[0102]

[0103] x w (n)=x r (n)-x f (n)

[0104] The broadband secondary sound source synthesis subsystem 2 includes a broadband controller 21 and a first filter-X minimum mean square algorithm module 22;

[0105] The broadband controller 21 employs a linear filter, the coefficients and length of which are respectively... and L w ;

[0106] The first filter-X minimum mean square algorithm module 22 utilizes the broadband reference signal x separated by the auxiliary filter subsystem 5. w (n) Related broadband residual noise component y h (n) is used as the error output and is used to update the coefficients of the broadband controller 21; the coefficient update formula of the broadband controller 21 is:

[0107]

[0108] Where, μ w This is the update step size for the broadband controller 21, and it takes a positive value. For the broadband reference signal x w (n) The output of the secondary channel estimation model in the first filter-X minimum mean square algorithm module 22;

[0109] The broadband secondary sound source obtained by the broadband secondary sound source synthesis subsystem 2 is:

[0110]

[0111] The narrowband secondary sound source synthesis subsystem 3 includes a narrowband controller 31 and a second filter-X minimum mean square algorithm module 32;

[0112] The narrowband controller 31 employs a linear filter, the coefficients and length of which are respectively... and L f ;

[0113] The second filtering-X least mean square algorithm module 32 utilizes the narrowband residual noise component y separated by the second linear prediction filtering subsystem 4. LP (n) is used as the error output and is used to update the coefficients of the narrowband controller 31; the coefficient update formula of the narrowband controller 31 is:

[0114]

[0115] Where, μ f This is the update step size for the narrowband controller 31, and it takes a positive value. For the narrowband reference signal x f (n) The output of the secondary channel estimation model in the second filter-X minimum mean square algorithm module 32;

[0116] The narrowband secondary sound source obtained by the narrowband secondary sound source synthesis subsystem 3 is...

[0117]

[0118] The second linear predictive filter subsystem 4 includes a second delay element 41 and a second linear predictive filter 42, which are connected in series. The order of the second delay element 41 is D2. The coefficients and length of the second linear predictive filter 42 are respectively... The coefficients of L2 and L3 are updated using the least mean square algorithm, and the update formula is as follows:

[0119] h 2,j (n+1)=h 2,j (n)+μ2e LP (n)e(n-D2-j)

[0120] Where μ2 is the update step size of the second linear prediction filter 42, and its value is positive; e LP e(n) represents the broadband residual noise component separated by the second linear prediction subsystem 4; e(n) represents the residual noise provided by the error microphone.

[0121] The narrowband residual noise component and the broadband residual noise component separated from the residual noise by the second linear predictive filtering subsystem 4 are as follows:

[0122]

[0123] e LP (n)=e(n)-y LP (n)

[0124] The broadband residual noise component e separated by the second linear predictive filter subsystem 4 LP (n), used as the desired input for the auxiliary filtering subsystem 5.

[0125] The auxiliary filtering subsystem 5 includes a linear filter 51 and a minimum mean square algorithm module 52;

[0126] The coefficients and length of the linear filter 51 are respectively And L3, the coefficients of the linear filter 51 are updated using the least mean square algorithm module 52, and the update formula is:

[0127] h 3,j (n+1)=h 3,j (n)+μ3e h (n)x w (nj)

[0128] Where μ3 is the update step size of the linear filter 51, and its value is positive; e h (n) represents the broadband residual noise component separated by the auxiliary filter subsystem 5, which is related to the additive noise in the auxiliary noise and the target signal;

[0129] The broadband residual noise component related to the broadband reference signal separated by the auxiliary filtering subsystem 5 is:

[0130]

[0131] The broadband residual noise component e, which is related to the additive noise in the auxiliary noise and the target signal, is separated by the auxiliary filtering subsystem 5. h (n), used as the expected input for the secondary channel online identification subsystem 6.

[0132] The secondary channel online identification subsystem 6 includes: a secondary channel online identification module 61 and an auxiliary noise adjustment module 62;

[0133] The secondary channel online identification module 61 includes a secondary channel estimation model. Secondary channel online identification module 61 (e) h (n) is the desired input, and the auxiliary noise v0(n) generated by the Gaussian white noise v(n) after passing through the auxiliary noise adjustment module 62 is the reference input. The time-varying secondary channel is estimated in real time using the least mean square algorithm.

[0134] Secondary channel estimation model of secondary channel online identification module 61 The coefficients and lengths are respectively and The formula for updating the coefficients is:

[0135]

[0136] e s (n)=e h (n)-y s (n)

[0137] Where, μ s This is the update step size for the secondary channel estimation model, and its value is positive; y s (n) represents the output of the secondary channel estimation model of the secondary channel online identification module 61;

[0138] The auxiliary noise v0(n) is:

[0139] v0(n)=v(n)G(n)

[0140] G(n) = max{G N (n),G B (n)}

[0141] G N (n)=abs[y LP (n-1)]

[0142] G B (n)=abs[y h (n-1)]

[0143] Wherein, G(n) is the gain adjustment factor of the auxiliary noise adjustment module 62; G N (n) and G B (n) represent y LP (n-1) and y h (n-1) is the output after low-pass filtering; abs[·] is the absolute value operation; v(n) is the output with zero mean and variance. Additive white Gaussian noise.

[0144] The synthesized secondary sound source is:

[0145] y(n)=y w (n)+y f (n)-v0(n)

[0146] Furthermore, the target noise p(n) and the signal y from the secondary sound source y(n) provided by the secondary loudspeaker after passing through the actual secondary channel S(z) are... p The difference between (n) is the residual noise, i.e., e(n) = p(n) - y p (n) can achieve active noise control by canceling interference in acoustic space.

[0147] Example 2:

[0148] This embodiment provides a robust feedforward hybrid wideband and narrowband active noise control method. The method is based on the robust feedforward hybrid wideband and narrowband active noise control system described above, and includes:

[0149] Step 1: Set system parameters

[0150] The following components are configured: a first linear prediction filter 12, a wideband controller 21, a narrowband controller 31, a second linear prediction filter 42, a linear filter 51, and a secondary channel estimation model. The length and step size are set; the order of the first delay element 11 and the first delay element 41 are set respectively; the forgetting factor of the auxiliary noise adjustment module 62 is set; the first linear prediction filter 12, the wideband controller 21, the narrowband controller 31, the second linear prediction filter 42, the linear filter 51, and the secondary channel estimation model are set respectively. The initial values ​​of the gain adjustment factor G(n) of the auxiliary noise adjustment module 62 are both zero; the auxiliary noise v(n) is set.

[0151] Step 2: Synthesize the wideband reference signal and the narrowband reference signal

[0152] At time n, the reference signal x obtained using the reference microphone r (n), which is separated into a broadband reference signal and a narrowband reference signal after passing through the first linear prediction filtering subsystem 1; the separated broadband reference signal is provided to the broadband secondary sound source synthesis subsystem 2 and the auxiliary filtering subsystem 5 respectively; the separated narrowband reference signal is provided to the narrowband secondary sound source synthesis subsystem 3;

[0153] Step 3: At time n, firstly, the broadband reference signal is synthesized into a broadband secondary sound source y by the broadband secondary sound source synthesis subsystem 2. w (n), the narrowband reference signal is synthesized into a narrowband secondary sound source y by the narrowband secondary sound source synthesis subsystem 3. f (n); secondly, auxiliary noise v0(n) is obtained using auxiliary noise adjustment module 62, and then compared with y w (n) and y f (n) are superimposed to synthesize the secondary sound source y(n); finally, the residual noise e(n) is separated by the second linear prediction filter subsystem 4 to obtain the narrowband residual noise component y. LP (n) and broadband residual noise component e LP (n);

[0154] Step 4: At time n, the broadband residual noise component e LP (n) After passing through the auxiliary filtering subsystem 5, broadband residual noise components e related to the auxiliary noise and additive noise in the target signal are obtained respectively. h (n), and the broadband residual noise component y related to the broadband reference signal. h (n); e h (n) is used as the expected input for the secondary channel online identification module 61;

[0155] Step 5: Update the control system

[0156] According to the reference signal xr (n) and the broadband reference signal x w (n) Calculate the coefficients of the updated first linear prediction filter 12 at time n+1;

[0157] Based on the broadband reference signal x w (n) and y h (n) Calculate the coefficients of the updated broadband controller 21 at time n+1;

[0158] Based on the narrowband reference signal x f (n) and narrowband residual noise y LP (n) Calculate the coefficients of the updated narrowband controller 31 at time n+1;

[0159] Based on the residual noise e(n) and the broadband residual noise component e LP (n) Calculate the coefficients of the updated second linear prediction filter 42 at time n+1;

[0160] Based on the broadband reference signal x w (n) and e h (n) Calculate the coefficients of the updated linear filter 51 at time n+1;

[0161] Based on the auxiliary noise v0(n) and e h (n) Calculate and update the secondary channel estimation model in the secondary channel online identification module 61. The coefficient at time n+1;

[0162] Based on the narrowband residual noise component y LP (n) and y h (n) Calculate the gain adjustment factor of the updated auxiliary noise adjustment module 62 at time n+1;

[0163] Step 6: Return to Step 2 and repeat Steps 2 through 5 until the system converges and reaches a steady state, thus achieving active noise control.

[0164] Example 3: Verification under Simulated Noise and Simulated Secondary Channel Conditions

[0165] The reference signal consists of three frequency components and additive noise. The normalized angular frequencies of its three frequency components are ω1 = 0.1π, ω2 = 0.2π, and ω3 = 0.3π, with corresponding discrete Fourier coefficients a1 = 2.0, b1 = -1.0, a2 = 1.0, b2 = -0.5, a3 = 0.5, and b3 = 0.1. Its additive noise is Gaussian white noise with zero mean and a variance of 0.25. The target noise p(n) contains additive Gaussian white noise v. pThe variance of (n) is 0.1. The actual primary channel P(z) uses an FIR model with a length and cutoff frequency of 41 and 0.4π, respectively. The actual secondary channel S(z) also uses an FIR model. To simulate the time-varying nature of the secondary channel, the length and cutoff frequency of this model are 21 and 0.4π in the first half and 11 and 0.4π in the second half, respectively. The secondary channel estimation model... The length is 31; the auxiliary Gaussian white noise v(n) has a mean of zero and a variance of 1.0. The broadband controller uses an FIR model with a length of 51; the narrowband controller uses an FIR model with a length of 21. The update step sizes for the narrowband controller, broadband controller, and secondary online identification module are 0.001, 0.006, and 0.0008, respectively. The first delay stage has an order of 5; the first linear prediction filter uses an FIR model with a length of 91 and an update step size of 0.0002. The second delay stage has an order of 32; the second linear prediction filter uses an FIR model with a length of 91 and an update step size of 0.0002. The auxiliary filter uses an FIR model with a length of 61 and an update step size of 0.003. The number of independent runs is 100; the length of the simulation sampling points is 30000.

[0166] Figure 2(a) shows the dynamic change curve of the mean square residual error in Example 3; Figure 2(b) shows the dynamic change curve of the mean square error of the secondary channel estimation in Example 3. Figure 2(a) and 2(b) As shown, after the system reaches steady state, the steady-state values ​​of the mean square residual error of the first and second halves of the system are 0.1041 and 0.1047, respectively, which are close to the variance of additive white Gaussian noise in the target noise, indicating that the system of the present invention has good suppression performance of mixed wide and narrow band noise. Figure 2(b) shows that the system of the present invention can not only effectively cope with the abrupt changes of the secondary channel, but also has good speed and accuracy of online identification of the secondary channel. Furthermore, it shows that the system of the present invention does not need to use non-acoustic sensors to obtain the frequency value of the prior narrowband reference component, thus overcoming the problem of frequency offset.

[0167] Example 4: Verification under actual target noise and actual secondary channel conditions

[0168] The actual noise originates from a large cutting machine. To simulate the non-stationary characteristics of the target noise, it is divided into two parts: the first part corresponds to a rotational speed of 1400 rpm, and the second part corresponds to a rotational speed of 1600 rpm. This target noise includes narrowband and wideband components. The actual primary channel P(z) adopts a linear FIR model, with a length and cutoff frequency of 61 and 0.45π, respectively. The target noise is the output of the actual reference signal after passing through the actual primary channel P(z). The actual secondary channel model adopts the widely used real IIR model (SMKuo and DRMorgan, Active Noise Control Systems - Algorithms and DSP Implementation, New York: Wiley, 1996). The secondary channel estimation model... The length is 32; the auxiliary Gaussian white noise v(n) has a mean of zero and a variance of 0.1. The broadband controller uses an FIR model with a length of 51; the narrowband controller uses an FIR model with a length of 21. The update step sizes of the narrowband controller, broadband controller, and secondary online identification module are 0.35, 0.35, and 0.07, respectively. The order of the first delay stage is 21; the first linear prediction filter uses an FIR model with a length of 21 and an update step size of 0.5. The order of the second delay stage is 21; the second linear prediction filter uses an FIR model with a length of 21 and an update step size of 0.5. The auxiliary filter uses an FIR model with a length of 41 and an update step size of 0.5. The number of independent runs is 100; the actual sampling length is 30000.

[0169] Figure 3 The figures show the dynamic variation curves of the target noise and residual noise in Example 4. After the system reaches steady state, the noise reduction of the first half of the system is 12.15 dB, and the noise reduction of the second half is 17.33 dB, indicating that the system of the present invention still has good wide-band and narrow-band mixed noise suppression performance under actual non-stationary target noise and actual secondary channel conditions. Furthermore, it shows that the system of the present invention does not require the use of non-acoustic sensors to obtain the frequency value of the prior narrow-band reference component, overcoming the problem of frequency offset.

[0170] The above embodiments three and four, through simulation and experiment respectively, jointly verify the effectiveness and practicality of the robust feedforward wide and narrow band hybrid active noise control system and method provided by the present invention, which will further promote the practical application of active noise control technology.

[0171] Some steps in the embodiments of the present invention can be implemented using software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0172] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A robust feed-forward wide narrowband hybrid active noise control system, characterized by, The active noise control system respectively collects reference signals by using reference microphones, collects residual noise by using error microphones, and provides secondary sound sources by using secondary loudspeakers; an actual primary channel in an acoustic space is a channel model of the reference signals propagating to the error microphones; an actual secondary channel in the acoustic space is a channel model of the secondary sound sources provided by the secondary loudspeakers propagating to the error microphones; the active noise control system comprises a first linear prediction filtering subsystem (1), a wideband secondary sound source synthesis subsystem (2), a narrowband secondary sound source synthesis subsystem (3), a second linear prediction filtering subsystem (4), an auxiliary filtering subsystem (5), and a secondary channel online identification subsystem (6); The first linear prediction filtering subsystem (1) is connected with the wideband secondary sound source synthesis subsystem (2), the narrowband secondary sound source synthesis subsystem (3), and the auxiliary filtering subsystem (5); the wideband secondary sound source synthesis subsystem (2) is connected with the first linear prediction filtering subsystem (1) and the auxiliary filtering subsystem (5); the narrowband secondary sound source synthesis subsystem (3) is connected with the first linear prediction filtering subsystem (1) and the second linear prediction filtering subsystem (4); the second linear prediction filtering subsystem (4) is connected with the narrowband secondary sound source synthesis subsystem (3), the auxiliary filtering subsystem (5), and the secondary channel online identification subsystem (6); the auxiliary filtering subsystem (5) is connected with the first linear prediction filtering subsystem (1), the wideband secondary sound source synthesis subsystem (2), the second linear prediction filtering subsystem (4), and the secondary channel online identification subsystem (6); the secondary channel online identification subsystem (6) is connected with the second linear prediction filtering subsystem (4) and the auxiliary filtering subsystem (5); The first linear prediction filtering subsystem (1) is used for synthesizing wideband reference signals and narrowband reference signals; the wideband secondary sound source synthesis subsystem (2) is used for synthesizing wideband secondary sound sources; the narrowband secondary sound source synthesis subsystem (3) is used for synthesizing narrowband secondary sound sources; the second linear prediction filtering subsystem (4) is used for separating narrowband residual noise components and wideband residual noise components from the residual noise; the auxiliary filtering subsystem (5) is used for separating wideband residual noise components related to the wideband reference signals and wideband residual noise components related to auxiliary noise and additive noise in target signals from the wideband residual noise components; and the secondary channel online identification subsystem (6) is used for estimating a time-varying secondary channel model in real time. The narrow-band residual noise component separated by the second linear prediction filtering subsystem (4) is used as the error output of the narrow-band secondary sound source synthesis subsystem (3) and the input of the auxiliary noise adjustment module in the secondary path online identification subsystem (6), respectively; meanwhile, the wide-band residual noise component separated by the auxiliary filtering subsystem (5) and related to the wide-band reference signal is used as the error output of the wide-band secondary sound source synthesis subsystem (2) and the input of the auxiliary noise adjustment module in the secondary path online identification subsystem (6), respectively; meanwhile, the wide-band residual noise component separated by the auxiliary filtering subsystem (5) and related to the auxiliary noise and the additive noise in the target signal is used as the expected input of the secondary path online identification subsystem (6); The first linear prediction filter subsystem (1) comprises a first delay link (11) and a first linear prediction filter (12), the first delay link (11) and the first linear prediction filter (12) are connected in series, the order of the first delay link (11) is ; the coefficient and length of the first linear prediction filter (12) are and respectively, the coefficient is updated by using a least mean square algorithm, and the update formula is: wherein is an update step size for the first linear prediction filter (12) and is positive; is a wideband reference signal separated by the first linear prediction subsystem (1), is a reference signal provided by the reference microphone; is a time instant, ; The narrow-band reference signal and the wide-band reference signal synthesized by the first linear prediction filtering subsystem (1) are: ； In the auxiliary filtering subsystem (5), a linear filter (51) and a least mean square algorithm module (52) are included; The coefficients and length of the linear filter (51) are respectively and The coefficient update of the linear filter (51) is performed by using the least mean square algorithm module (52), and the update formula is: wherein is an update step size of the linear filter (51) and is a positive value; is a wideband residual noise component related to the auxiliary noise and the additive noise in the target signal separated by the auxiliary filtering subsystem (5); The wide-band residual noise component separated by the auxiliary filtering subsystem (5) and related to the wide-band reference signal is: The auxiliary filtering subsystem (5) separates a wideband residual noise component related to the auxiliary noise and to the additive noise in the target signal to be used as the desired input of the secondary path online identification subsystem (6).

2. A robust feed-forward wide narrowband hybrid active noise control system as claimed in claim 1, characterized in that, In the wide-band secondary sound source synthesis subsystem (2), a wide-band controller (21) and a first filter-X least mean square algorithm module (22) are included; The wideband controller (21) employs a linear filter whose coefficients and length are respectively and ; said first filtered-X least mean square algorithm module (22) using the wideband reference signal a wideband residual noise component as an error output and for updating coefficients of the wideband controller (21); the coefficient update formula of the wideband controller (21) is: wherein, is an update step size of the wideband controller (21) and is a positive value; is the wideband reference signal is the output of the secondary channel estimation model in the first filtered-X least mean square algorithm module (22). The wide-band secondary sound source obtained by the wide-band secondary sound source synthesis subsystem (2) is: 。 3. A robust feed-forward wide narrowband hybrid active noise control system as claimed in claim 2, characterized in that, In the narrow-band secondary sound source synthesis subsystem (3), a narrow-band controller (31) and a second filter-X least mean square algorithm module (32) are included; The narrowband controller (31) employs a linear filter whose coefficients and length are respectively and ; The second filter-X least mean square algorithm module (32) uses the narrowband residual noise component separated by the second linear prediction filter subsystem (4) as an error output and used to update the coefficients of the narrowband controller (31); the coefficient update formula of the narrowband controller (31) is: wherein, is an update step size of the narrowband controller (31) and is a positive value; is the narrowband reference signal is an output of a secondary channel estimation model in the second filtered-X least mean square algorithm module (32). The narrow-band secondary sound source obtained by the narrow-band secondary sound source synthesis subsystem (3) is: 。 4. A robust feed-forward wide narrowband hybrid active noise control system as defined in claim 3, wherein, The second linear prediction filter subsystem (4) comprises a second delay link (41) and a second linear prediction filter (42), the second delay link (41) and the second linear prediction filter (42) are connected in series, the order of the second delay link (41) is ; the coefficient and length of the second linear prediction filter (42) are and respectively, the coefficient is updated by using a least mean square algorithm, and the update formula is: wherein is an update step size for the second linear prediction filter (42) and is positive; is a wideband residual noise component separated out by the second linear prediction subsystem (4); is residual noise provided by the error microphone; The narrow-band residual noise component and the wide-band residual noise component separated by the second linear prediction filtering subsystem (4) from the residual noise are: The second linear prediction filter subsystem (4) separates a wideband residual noise component is used as the desired input to the auxiliary filter subsystem (5).

5. The robust feed-forward wide and narrow band hybrid active noise control system according to claim 4, wherein, The secondary path online identification subsystem (6) includes a secondary path online identification module (61) and an auxiliary noise adjustment module (62); The secondary channel online identification module (61) includes a secondary channel estimation model. The secondary channel online identification module (61) uses... As the desired input, use Gaussian white noise The auxiliary noise generated after passing through the auxiliary noise adjustment module (62) Using the least mean square algorithm as a reference input, the time-varying secondary channel is estimated in real time. The secondary channel estimation model of the secondary channel online identification module (61) The coefficients and length of the secondary channel estimation model are and The coefficient update formula is: wherein is an update step for the secondary path estimation model, taking positive values; is an output of the secondary path estimation model of the secondary path online identification module (61); The auxiliary noise Is: wherein is a gain adjustment factor for the auxiliary noise adjustment module (62); are is the low-pass filtered output; is an absolute value operation; is an additive white Gaussian noise with zero mean and variance .​​ 6. The robust feed-forward wide and narrow band hybrid active noise control system according to claim 5, wherein, The synthesized secondary sound source is: Further Output to the secondary speaker, to interfere destructively with the target noise within the acoustic space.

7. An active noise control method, characterized by, The method is based on the robust feed-forward wide / narrow-band hybrid active noise control system of claim 6, and the method comprises: Step one: setting system parameters; The following components are configured: a first linear prediction filter (12), a wideband controller (21), a narrowband controller (31), a second linear prediction filter (42), a linear filter (51), and a secondary channel estimation model. The length and step size are set; the order of the first delay stage (11) and the first delay stage (41) are set respectively; the forgetting factor of the auxiliary noise adjustment module (62) is set; the first linear prediction filter (12), the wideband controller (21), the narrowband controller (31), the second linear prediction filter (42), the linear filter (51), and the secondary channel estimation model are set respectively. and the gain adjustment factor of the auxiliary noise adjustment module (62). The initial values ​​are all zero; auxiliary noise is set. ; Step two: synthesizing a wide-band reference signal and a narrow-band reference signal; At the reference signal obtained by the reference microphone is separated into a wideband reference signal and a narrowband reference signal by the first linear prediction filtering subsystem (1); the separated wideband reference signal is provided to the wideband secondary sound source synthesis subsystem (2) and the auxiliary filtering subsystem (5); the separated narrowband reference signal is provided to the narrowband secondary sound source synthesis subsystem (3); Step 3: In At any given moment, the broadband reference signal is first processed by the broadband secondary sound source synthesis subsystem (2) to obtain the broadband secondary sound source. The narrowband reference signal is synthesized into a narrowband secondary sound source by the narrowband secondary sound source synthesis subsystem (3). Secondly, auxiliary noise is obtained using the auxiliary noise adjustment module (62). and then with and Superposition and synthesis yield a secondary sound source. Finally, residual noise The narrowband residual noise component is obtained by separation through the second linear predictive filtering subsystem (4). and broadband residual noise components ; Step four: at the same time, the wideband residual noise component related to the additive noise in the target signal is obtained after the auxiliary filtering subsystem (5) respectively , and the wideband residual noise component related to the wideband reference signal ; serves as the desired input for the secondary path online identification module (61); Step five: updating the control system; According to the reference signal and the wideband reference signal calculating updated coefficients of the first linear prediction filter (12) at the time instant; According to the wideband reference signal and a wideband residual noise component computing updated coefficients of the wideband controller (21) at the time instant; According to the narrowband reference signal and narrowband residual noise calculating updates the coefficients of the narrowband controller (31) at the instant According to the residual noise and the wideband residual noise component computing updated coefficients of the second linear prediction filter (42) at the time instant; According to the wideband reference signal And Computing updates the coefficients of the linear filter (51) at Time instant; According to the auxiliary noise And Computing updates the secondary path estimate model in the secondary path online identification module (61) At The coefficient of the moment According to the narrowband residual noise component And Compute an update of the gain adjustment factor of the auxiliary noise adjustment module (62) at the moment; Step six: returning to the step two, repeating the steps two to five until the system converges and reaches a steady state, and realizing active noise control.

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