A narrow-band feedforward-feedback hybrid active noise control system and method

Abstract

The application discloses a narrow-band feedforward-feedback hybrid active noise control system and method, and belongs to the technical field of active noise control. The system separates the narrow-band residual noise component related to the reference signal and the residual noise component unrelated to the reference signal from the residual noise by using an auxiliary filtering subsystem; the system separates the narrow-band residual noise component and the wide-band residual noise component from the residual noise component unrelated to the reference signal by using a linear prediction filtering subsystem; the system improves the independence among the feedforward active noise control subsystem, the feedback active noise control subsystem and the secondary path online identification subsystem by using the auxiliary filtering subsystem and the linear prediction filtering subsystem, improves the dynamic performance of the system, and introduces an auxiliary noise adjustment module to improve the noise suppression performance of the system. The application effectively reduces the narrow-band target noise components related to the reference signal and unrelated to the reference signal, improves the noise reduction performance, and widens the application range.

Description

Technical Field

[0001] This invention relates to a narrowband forward feedback hybrid active noise control system and method, belonging to the field of active noise control technology. Background Technology

[0002] With the continuous advancement of technologies such as electroacoustics, integrated circuits, and intelligent signal processing, active noise control (ANC) technology has been widely applied in noise reduction in automobiles, rotating machinery, and other applications. ANC technology 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. It is 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 pastdecade, Part I: Linear systems,” Signal Process. 183(2021), 108039.). Based on the controller structure, ANC systems can be classified into three types: feedforward, feedback, and hybrid feedforward / feedback.

[0003] In real-world factories, there are numerous periodic harmful noises generated by rotating equipment (such as cutting machine noise and engine noise), typically dominated by narrowband components. In such cases, traditional narrowband feedforward ANC systems, using non-acoustic sensors (such as tachometers) to acquire reference signals, employ controller structures based on discrete Fourier coefficients to address these narrowband target noises (SM Kuo and DR Morgan, “Active noise control: a tutorial review,” Proc. IEEE, vol.87, no.6, pp.943-973, Jun. 1999). However, under certain operating conditions, the narrowband target noise generated by rotating machinery may simultaneously contain narrowband target noise components related to the reference signal and those independent of it, with similar energies. In such situations, using a traditional narrowband feedforward ANC system would suppress its noise reduction performance. Therefore, a feedback active noise control subsystem is needed to address the narrowband target noise component independent of the reference signal. Therefore, developing a high-performance narrowband forward-feedback hybrid ANC system that combines feedforward and feedback has greater practical application value.

[0004] A narrowband forward-feedback hybrid ANC system employs a feedforward active noise control subsystem to handle narrowband target noise separation related to the reference signal, and a feedback active noise control subsystem to handle narrowband frequency separation unrelated to the reference signal. A spark noise canceller is introduced to provide a reliable reference input for the feedback active noise control subsystem and a reliable error output for the feedforward active noise control subsystem. This improves the independence of the two subsystems (T. Padhi, M. Chandra, A. Kar, and MNS Swamy, “A new adaptive control strategy for hybrid narrowband active noise control systems in a multi-noise environment,” Applied Acoustics, vol. 146, pp. 355-367, 2019.). However, the system still has the following problems: 1. Introducing a spark noise canceller can separate narrowband residual noise components related to the reference signal, but the separated residual noise components unrelated to the reference signal still contain both narrowband residual noise components unrelated to the reference signal and broadband noise related to additive noise in the target noise. If the residual noise components unrelated to the reference signal separated by the spark noise canceller are directly used to synthesize the reference signal of the feedback active noise control subsystem and update the feedback controller, the quality of the synthesized reference signal will be reduced, and the feedforward active noise control subsystem and the feedback active noise control subsystem will be affected. 1. Poor independence between components affects the overall system convergence performance; 2. If the same frequency components in the narrowband target noise come from different noise sources, that is, the two narrowband components in the narrowband target noise are correlated, the noise reduction performance of the above narrowband front feedback hybrid ANC system will deteriorate under this condition, and may even lead to system instability; 3. This system assumes that the secondary channel is obtained through offline identification. However, in actual working conditions, the secondary channel has complex time-varying characteristics, which will also seriously affect the stability of the system. Designing an efficient online identification method for the secondary channel of this narrowband front feedback hybrid ANC system has important theoretical and application value.

[0005] To simultaneously address the issues that constrain system performance, such as poor independence of the aforementioned control subsystems, insufficient narrowband target noise suppression performance, and the complex time-varying nature of secondary channels, a more effective and practical narrowband forward feedback hybrid active noise control system is needed. Summary of the Invention

[0006] To address the problems of poor independence of control subsystems, insufficient narrowband target noise suppression performance, and complex time-varying nature of secondary channels in traditional narrowband front-feedback hybrid active noise control systems, which severely restrict the convergence and stability of the system and reduce the overall narrowband target noise suppression performance, this invention provides a narrowband front-feedback hybrid active noise control system and method.

[0007] The first objective of this invention is to provide a narrowband forward feedback hybrid active noise control system, comprising:

[0008] A feedforward active noise control subsystem is used to synthesize a narrowband secondary sound source related to the reference signal;

[0009] Feedback active noise control subsystem is used to synthesize narrowband secondary sound sources that are independent of the reference signal;

[0010] An auxiliary filtering subsystem is used to separate the narrowband residual noise component related to the reference signal and the residual noise component unrelated to the reference signal from the residual noise.

[0011] A linear predictive filtering subsystem is used to separate narrowband and wideband residual noise components from residual noise components independent of the reference signal; and

[0012] The secondary channel online identification subsystem is used to estimate the time-varying actual secondary channel online, and includes an auxiliary noise amplitude adjustment module;

[0013] The auxiliary filtering subsystem is connected to the feedforward active noise control subsystem, the linear predictive filtering subsystem, and the secondary channel online identification subsystem. The narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem is used as the error output of the feedforward active noise control subsystem and the input of the auxiliary noise amplitude adjustment module of the secondary channel online identification subsystem, respectively. At the same time, the residual noise component unrelated to the reference signal separated by the auxiliary filtering subsystem is used as the input of the linear predictive filtering subsystem.

[0014] The linear predictive filtering subsystem is connected to the feedback active noise control subsystem and the secondary channel online identification subsystem. The narrowband residual noise component, which is independent of the reference signal, separated by the linear predictive filtering subsystem, is used as the error output of the feedback active noise control subsystem and the input of the auxiliary noise amplitude adjustment module of the secondary channel online identification subsystem, respectively. At the same time, the broadband residual noise component separated by the linear predictive filtering subsystem is used as the desired input of the secondary channel online identification subsystem. This can improve the independence between the feedforward active noise control subsystem, the feedback active noise control subsystem, and the secondary channel online identification subsystem, improve the dynamic performance of the system, and at the same time, the introduction of the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem reduces the contribution of auxiliary noise to residual noise, effectively reduces the narrowband target noise components that are related to and independent of the reference signal, and improves the noise suppression performance of the system.

[0015] In one embodiment of the present invention, the feedforward active noise control subsystem includes a feedforward controller and a first filter-X least mean square algorithm module;

[0016] The feedforward controller is represented using discrete Fourier coefficients, which are: ,in The number of narrowband frequencies of the reference signal; For a moment, ;

[0017] The first filter-X least mean square algorithm module uses the narrowband residual noise component related to the reference signal separated by the auxiliary filter subsystem. As an error output, it is used to update the feedforward controller; the coefficient update formula of the feedforward controller is:

[0018]

[0019] in, This is the update step size of the feedforward controller, and it takes a positive value. Reference signal The outputs of the secondary channel estimation model in the first filter-X minimum mean square algorithm module are respectively processed; ; The first reference signal A narrowband frequency;

[0020] The feedforward active noise control subsystem obtains the narrowband secondary sound source related to the reference signal as follows:

[0021] .

[0022] In one embodiment of the present invention, the feedback active noise control subsystem includes an internal reference synthesis module, a feedback controller, and a second filter-X least mean square algorithm module. The internal reference synthesis module includes a secondary channel estimation model and a first-order delay element, used to synthesize an internal reference signal. ,Right now

[0023]

[0024] in, The output of the feedback controller is the output of the secondary channel estimation model. The narrowband residual noise component that is independent of the reference signal is separated by the auxiliary filtering subsystem;

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

[0026] The second filter-X least mean square algorithm module uses the narrowband residual noise component that is independent of the reference signal separated by the auxiliary filtering subsystem. As an error output, it is used to update the coefficients of the feedback controller; the coefficient update formula of the feedback controller is:

[0027]

[0028] in, This is the update step size of the feedback controller, and it takes a positive value; Provided for the internal reference synthesis module The output of the secondary channel estimation model in the second filter-X least mean square algorithm module;

[0029] The narrowband secondary sound source obtained by the feedback active noise control subsystem, which is independent of the reference signal, is:

[0030] .

[0031] In one embodiment of the present invention, the auxiliary filtering subsystem includes a Fourier analyzer and a variable step-size minimum mean square algorithm module; the Fourier analyzer uses discrete Fourier coefficients, i.e. ;

[0032] The variable step size least mean square algorithm module uses the broadband residual noise component that is independent of the reference signal, separated by the auxiliary filtering subsystem. As an error output, it is used to update the Fourier analyzer; the coefficient update formula of the Fourier analyzer is:

[0033]

[0034] in, The update step size of the Fourier analyzer is set to a positive value; the variable step size minimum mean square algorithm module is used to update the step size. Real-time updates are performed, and the step size update formula is as follows:

[0035]

[0036] in, These are user parameters, and all are positive values ​​less than 1.

[0037] The narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem is:

[0038]

[0039] The residual noise component that is independent of the reference signal separated by the auxiliary filtering subsystem is:

[0040] ;

[0041] in, for Residual noise at any given moment;

[0042] Meanwhile, the narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem is used as the input of the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem.

[0043] In one embodiment of the present invention, the linear predictive filtering subsystem includes a delay element and a linear predictive filter, wherein the delay element and the linear predictive filter are cascaded, and the order of the delay element is [missing information]. The coefficients and length of the linear prediction filter are respectively and Its coefficients are updated using the least mean square algorithm, that is:

[0044]

[0045] in, The update step size of the linear prediction filter is a positive value. The broadband residual noise component separated from the linear prediction subsystem;

[0046] The narrowband residual noise component and the broadband residual noise component, which are independent of the reference signal and are separated from the residual noise by the linear predictive filtering subsystem, are respectively...

[0047]

[0048]

[0049] The narrowband residual noise component, which is independent of the reference signal, separated by the linear predictive filtering subsystem, is used as the input to the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem; at the same time, the broadband residual noise component separated by the linear predictive filtering subsystem is used as the desired input to the secondary channel online identification subsystem.

[0050] In one embodiment of the present invention, the secondary channel online identification subsystem further includes a secondary channel online identification module;

[0051] The secondary channel online identification module includes a secondary channel estimation model. The secondary channel online identification module is based on... As the desired input, use Gaussian white noise The auxiliary noise generated after passing through the auxiliary noise amplitude adjustment module Using the least mean square algorithm as the reference input, the time-varying actual secondary channel is estimated online.

[0052] The secondary channel estimation model of the secondary channel online identification module A linear filter is used, with coefficients and length respectively. and The formula for updating its coefficients is:

[0053]

[0054]

[0055] in, This is the update step size for the secondary channel estimation model, and its value is positive. This is the output of the secondary channel estimation model of the secondary channel online identification module; This is the error output of the secondary channel online identification module;

[0056] The auxiliary noise for:

[0057]

[0058]

[0059] in, With a mean of zero and a variance of Additive white Gaussian noise; The adjustment gain of the auxiliary noise amplitude adjustment module; Forgetting factor, ; It is the power exponent, with a value of 1 or 2.

[0060] In one embodiment of the present invention, the synthesized secondary sound source is:

[0061]

[0062] Target noise Secondary sound source provided by secondary speakers Through the actual secondary channel The signal after In acoustic space, interference cancels out, thus obtaining residual noise. To achieve active noise control;

[0063] Among them, the actual secondary channel This represents the acoustic space model from the secondary loudspeaker to the error microphone;

[0064] The target noise is:

[0065]

[0066] in, Within the acoustic space The reference signal corresponding to each narrowband frequency passes through the actual secondary channel. The signal that propagates to the error microphone; For acoustic space and Narrowband target noise components that are related to or unrelated to narrowband frequencies; With a mean of zero and a variance of Additive white Gaussian noise.

[0067] In one embodiment of the present invention, the system monitors abrupt changes in the secondary channel model or target noise online by calculating the energy change of the residual noise after smoothing filtering in real time, so as to adjust the coefficients of the feedforward controller, the Fourier analyzer, the linear prediction filter, and the secondary channel estimation model. The coefficients and the adjustment gain of the auxiliary noise amplitude adjustment module are reinitialized;

[0068] The energy of the residual noise after smoothing filtering is:

[0069]

[0070] in, Forgetting factor in smoothing filtering;

[0071] exist At any time, according to After performing time averaging and smoothing filtering, we get:

[0072]

[0073] in, Positive integers greater than 1 The time-averaged window length, For a moment, and ;

[0074] when Always satisfied At that time, The timekeeping system is reinitialized; among them, This is the threshold parameter.

[0075] In one embodiment of the present invention, the active noise control system uses a non-acoustic 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 secondary channel in the acoustic space is a channel model for the propagation of the secondary sound source to the error microphone.

[0076] A second objective of this invention is to provide a narrowband forward feedback hybrid active noise control method, wherein the method applies the aforementioned narrowband forward feedback hybrid active noise control system, and the method includes:

[0077] Step 1: Set system parameters;

[0078] Set the update step size for the feedforward controller and Fourier analyzer; set the feedback controller, linear prediction filter, and secondary channel estimation model respectively. Length and step size; set the order of the delay stage; set the forgetting factor for the auxiliary noise amplitude adjustment module. Power index Set up a feedforward controller, a feedback controller, a Fourier analyzer, a linear prediction filter, and a secondary channel estimation model respectively. And the adjustment gain of the auxiliary noise amplitude adjustment module. The initial values ​​are all zero; auxiliary noise is set. ;

[0079] Step 2: Obtain the reference signal;

[0080] exist At any given time, a narrowband reference frequency is obtained using a non-acoustic sensor. ; Employing an internal reference synthesis module and Residual noise at time and Obtain internal reference signal The amplitude adjustment gain is obtained by using an auxiliary noise amplitude adjustment module. ;

[0081] Step 3: In At any given moment, the feedforward controller first provides a narrowband secondary sound source related to the reference signal. The feedback controller provides a narrowband secondary sound source independent of the reference signal. Secondly, the auxiliary noise is obtained using the auxiliary noise amplitude adjustment module. ,and then , and The three components combined produce a secondary sound source. Finally, residual noise The narrowband residual noise component related to the reference signal is obtained by the auxiliary filtering subsystem. and residual noise components independent of the reference signal ;

[0082] Step Four: In At time , residual noise components independent of the reference signal The linear predictive filtering subsystem obtains narrowband residual noise components independent of the reference signal. Broadband residual noise components related to auxiliary noise and additive noise in the target signal. ; Used as input for the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem; Used as the expected input for the secondary channel online identification module;

[0083] Step 5: Update the control system;

[0084] According to the reference signal and narrowband residual noise components related to the reference signal Calculate and update the feedforward controller in The coefficient at time;

[0085] Based on the internally synthesized reference signal and narrowband residual noise components independent of the reference signal Calculate and update the feedback controller in The coefficient at time;

[0086] Based on residual noise components independent of the reference signal and broadband residual noise components independent of the reference signal Calculate and update the linear prediction filter in The coefficient at time;

[0087] According to auxiliary noise and Calculate and update the secondary channel estimation model of the secondary channel online identification module. exist The coefficient at time;

[0088] Based on the narrowband residual noise component related to the reference signal and narrowband residual noise components independent of the reference signal The auxiliary noise amplitude adjustment module is calculated and updated in [the following context]. Adjusting the gain at any given moment;

[0089] Step Six: Monitor abrupt changes in the actual secondary channel or target noise;

[0090] Real-time calculation of the change in residual noise energy after smoothing filtering, if it satisfies Then in The coefficients of the feedforward controller, the Fourier analyzer, the linear prediction filter, and the secondary channel estimation model at each time step. The coefficients and the adjustment gain of the auxiliary noise amplitude adjustment module are reinitialized, and then proceed to step seven; if the conditions are met... If so, proceed directly to step seven;

[0091] Step 7: Return to Step 2 and repeat Steps 2 through 6 until the system gradually converges and reaches a steady state, thus achieving noise control.

[0092] The beneficial effects of this invention are as follows:

[0093] (1) The present invention does not require the setting of a reference microphone, which effectively avoids the problem of acoustic feedback.

[0094] (2) The present invention uses the narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem to update the feedforward controller, uses the narrowband residual noise component unrelated to the reference signal separated by the linear predictive filtering subsystem to update the feedback controller, and uses the broadband residual noise component related to the auxiliary noise and additive noise in the target signal separated by the linear predictive filtering subsystem as the expected input of the secondary channel online identification module. This improves the independence between the feedforward active noise control subsystem, the feedforward active noise control subsystem, and the secondary channel online identification subsystem, improves the accuracy and speed of the secondary channel online identification, and also improves the dynamic performance of the overall system.

[0095] (3) The present invention simultaneously uses the narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem and the narrowband residual noise component unrelated to the reference signal separated by the linear prediction filtering subsystem, which are used together for auxiliary noise amplitude adjustment. This effectively reduces the contribution of the introduced auxiliary noise to the residual noise, effectively reduces the narrowband target noise components related to and unrelated to the reference signal, and improves the overall noise reduction performance of the system. Theoretically, it can realize that the residual noise energy after the system reaches steady state tends to the environmental level.

[0096] (4) The present invention is equipped with a secondary channel online identification module, which can effectively solve the complex time-varying problem of the secondary channel under actual working conditions and meet the actual application requirements.

[0097] (5) This invention can not only reduce narrowband target noise containing unrelated frequency components, but also effectively deal with narrowband target noise containing related frequency components, thus broadening the scope of noise reduction applications. Attached Figure Description

[0098] 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.

[0099] Figure 1 This is a schematic diagram of a narrowband front feedback hybrid active noise control system provided in Embodiment 1 of the present invention.

[0100] Figure 2 This is a dynamic change curve of the residual noise mean square error in Embodiment 3 of the present invention.

[0101] Figure 3 This is a dynamic change curve of the mean square error of the secondary channel estimation in Embodiment 3 of the present invention.

[0102] Figure 4 This is a dynamic change curve of the residual noise mean square error in Embodiment 4 of the present invention.

[0103] Figure 5 This is a dynamic change curve of the mean square error of the secondary channel estimation in Embodiment 4 of the present invention.

[0104] In the diagram: 1. Feedforward active noise control subsystem; 2. Feedback active noise control subsystem; 3. Auxiliary filtering subsystem; 4. Linear predictive filtering subsystem; 5. Secondary channel online identification subsystem; 11. Feedforward controller; 12. First filter - X minimum mean square algorithm module; 21. Internal reference synthesis module; 22. Feedback controller; 23. Second filter - X minimum mean square algorithm module; 31. Fourier analyzer; 32. Variable step size minimum mean square algorithm module; 41. Delay element; 42. Linear predictive filter; 51. Secondary channel online identification module; 52. Auxiliary noise amplitude adjustment module. Detailed Implementation

[0105] The invention is described in detail below. In the following paragraphs, different aspects of the embodiments are defined in more detail. The aspects so defined may be combined with any other aspect or aspects unless explicitly stated otherwise. In particular, any feature considered preferred or advantageous may be combined with one or more other features considered preferred or advantageous.

[0106] The terms "first" and "second" used in this invention are merely for ease of description and to distinguish different components with the same name, and do not indicate a sequential or primary / secondary relationship.

[0107] Furthermore, when an element is referred to as being "on" another element, the element may be directly on the other element, or it may be indirectly on the other element with one or more intermediate elements inserted between them. Additionally, when an element is referred to as being "connected" to another element, the element may be directly connected to the other element, or it may be indirectly connected to the other element with one or more intermediate elements inserted between them. In the following drawings, the same reference numerals denote the same elements.

[0108] Example 1:

[0109] like Figure 1 As shown, this embodiment provides a narrowband front-feedback hybrid active noise control system. The active noise control system uses a non-acoustic 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 secondary channel in the acoustic space is a channel model of the secondary sound source propagating to the error microphone. The active noise control system includes a feedforward active noise control subsystem 1, a feedback active noise control subsystem 2, an auxiliary filtering subsystem 3, a linear predictive filtering subsystem 4, and a secondary channel online identification subsystem 5.

[0110] Feedforward active noise control subsystem 1 is connected to auxiliary filtering subsystem 3; feedback active noise control subsystem 2 is connected to linear predictive filtering subsystem 4; auxiliary filtering subsystem 3 is connected to feedforward active noise control subsystem 1, linear predictive filtering subsystem 4, and secondary channel online identification subsystem 5 respectively; linear predictive filtering subsystem 4 is connected to feedback active noise control subsystem 2, auxiliary filtering subsystem 3, and secondary channel online identification subsystem 5 respectively; secondary channel online identification subsystem 5 is connected to auxiliary filtering subsystem 3 and linear predictive filtering subsystem 4 respectively.

[0111] Feedforward active noise control subsystem 1 is used to synthesize narrowband secondary sound sources related to the reference signal; feedback active noise control subsystem 2 is used to synthesize narrowband secondary sound sources independent of the reference signal; auxiliary filtering subsystem 3 is used to separate narrowband residual noise components related to the reference signal and residual noise components independent of the reference signal from the residual noise; linear prediction filtering subsystem 4 is used to separate narrowband residual noise components and broadband residual noise components independent of the reference signal; secondary channel online identification subsystem 5 is used to estimate the time-varying actual secondary channel online;

[0112] The narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem 3 is used as the error output of the feedforward active noise control subsystem 1 and the input of the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5, respectively. Simultaneously, the residual noise component independent of the reference signal separated by the auxiliary filtering subsystem 3 is used as the input of the linear prediction filtering subsystem 4; the narrowband residual noise component independent of the reference signal separated by the linear prediction filtering subsystem 4 is used as the error output of the feedback active noise control subsystem 2 and the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5, respectively. The input of the adjustment module is adjusted; at the same time, the broadband residual noise component separated by the linear predictive filtering subsystem 4 is used as the desired input of the secondary channel online identification subsystem 5; this can improve the independence between the feedforward active noise control subsystem 1, the feedback active noise control subsystem 2, and the secondary channel online identification subsystem 3, and improve the dynamic performance of the system. At the same time, the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5 is introduced to reduce the contribution of auxiliary noise to residual noise, effectively reduce the narrowband target noise components related to and unrelated to the reference signal, and improve the noise suppression performance of the system.

[0113] Actual secondary channel This represents the acoustic space model from the secondary loudspeaker to the error microphone.

[0114] The target noise is:

[0115]

[0116] in, Within the acoustic space The reference signal corresponding to each narrowband frequency passes through the actual secondary channel. The signal that propagates to the error microphone; For acoustic space and Narrowband target noise components that are related to or unrelated to narrowband frequencies; With a mean of zero and a variance of Additive white Gaussian noise; For a moment, .

[0117] The feedforward active noise control subsystem 1 includes a feedforward controller 11 and a first filter-X minimum mean square algorithm module 12;

[0118] The feedforward controller 11 is represented by discrete Fourier coefficients, which are: ,in The number of narrowband frequencies of the reference signal;

[0119] The first filter-X minimum mean square algorithm module 12 uses the narrowband residual noise component related to the reference signal separated by the auxiliary filter subsystem 3. As an error output, it is used to update the feedforward controller 11; the coefficient update formula of the feedforward controller 11 is:

[0120]

[0121] in, This is the update step size for the feedforward controller 31, and it takes a positive value. Reference signal The outputs of the secondary channel estimation models in the first filter-X minimum mean square algorithm module 12 are respectively processed; ; The first reference signal A narrowband frequency;

[0122] The feedforward active noise control subsystem 1 obtains the narrowband secondary sound source related to the reference signal as follows:

[0123] .

[0124] The feedback active noise control subsystem 2 includes an internal reference synthesis module 21, a feedback controller 22, and a second filter-X least mean square algorithm module 23. The internal reference synthesis module 21 includes a secondary channel estimation model and a first-order delay element, used to synthesize the internal reference signal. ,Right now

[0125]

[0126] in, The output of feedback controller 22 is the output of the secondary channel estimation model. The narrowband residual noise component that is independent of the reference signal is separated by the auxiliary filtering subsystem 4;

[0127] The feedback controller 22 employs a linear filter, the coefficients and length of which are respectively... and ;

[0128] The second filtering-X minimum mean square algorithm module 23 uses the narrowband residual noise component that is independent of the reference signal separated by the auxiliary filtering subsystem 4. As an error output, it is used to update the coefficients of feedback controller 22; the coefficient update formula for feedback controller 22 is:

[0129]

[0130] in, This is the update step size for the feedback controller 22, and it takes a positive value. Provided for internal reference synthesis module 21 The output of the secondary channel estimation model in the second filter-X minimum mean square algorithm module 23;

[0131] The narrowband secondary sound source independent of the reference signal obtained by the feedback active noise control subsystem 2 is:

[0132] .

[0133] The auxiliary filtering subsystem 3 includes a Fourier analyzer 31 and a variable step-size minimum mean square algorithm module 32; the Fourier analyzer 31 uses discrete Fourier coefficients, i.e. ;

[0134] The variable step size minimum mean square algorithm module 32 uses the broadband residual noise component that is independent of the reference signal separated by the auxiliary filtering subsystem 3. As an error output, it is used to update the Fourier analyzer 31; the coefficient update formula for the Fourier analyzer 31 is:

[0135]

[0136] in, The update step size of the Fourier analyzer 31 is set to a positive value; the step size is adjusted using the variable step size minimum mean square algorithm module 32. Real-time updates are performed, and the step size update formula is as follows:

[0137]

[0138] in, These are user parameters, and all are positive values ​​less than 1.

[0139] The narrowband residual noise component related to the reference signal separated by auxiliary filter subsystem 3 is:

[0140]

[0141] The residual noise component independent of the reference signal separated by auxiliary filter subsystem 3 is:

[0142] ;

[0143] in, for Residual noise at any given moment;

[0144] Meanwhile, the narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem 3 is used as the input to the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5.

[0145] The linear predictive filter subsystem 4 includes a delay element 41 and a linear predictive filter 42, which are cascaded together. The order of the delay element 41 is... The coefficients and length of the linear prediction filter 42 are respectively and Its coefficients are updated using the least mean square algorithm, that is:

[0146]

[0147] in, This is the update step size for the linear prediction filter 42, and it takes a positive value. The broadband residual noise component separated from the linear prediction subsystem 4;

[0148] The narrowband and wideband residual noise components, which are independent of the reference signal and are separated from the residual noise by the linear predictive filter subsystem 4, are respectively

[0149]

[0150]

[0151] Meanwhile, the narrowband residual noise component, which is independent of the reference signal, separated by the linear predictive filtering subsystem 4 is used as the input of the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5; at the same time, the broadband residual noise component separated by the linear predictive filtering subsystem 4 is used as the desired input of the secondary channel online identification subsystem 5.

[0152] The secondary channel online identification subsystem 5 includes a secondary channel online identification module 51 and an auxiliary noise amplitude adjustment module 52;

[0153] The secondary channel online identification module 51 includes a secondary channel estimation model. Secondary channel online identification module 51 As the desired input, use Gaussian white noise Auxiliary noise generated after auxiliary noise amplitude adjustment module 52 Using the least mean square algorithm as the reference input, the time-varying actual secondary channel is estimated online.

[0154] Secondary channel estimation model of secondary channel online identification module 51 A linear filter is used, with coefficients and length respectively. and The formula for updating its coefficients is:

[0155]

[0156]

[0157] in, This is the update step size for the secondary channel estimation model, and its value is positive. This is the output of the secondary channel estimation model of the secondary channel online identification module 51. This is the error output of the secondary channel online identification module 51;

[0158] auxiliary noise for:

[0159]

[0160]

[0161] in, With a mean of zero and a variance of Additive white Gaussian noise; To assist the noise amplitude adjustment module 52 in adjusting its gain; Forgetting factor, ; It is the power exponent, with a value of 1 or 2.

[0162] The synthesized secondary sound source is:

[0163]

[0164] Furthermore, target noise Secondary sound source provided by secondary speakers Through the actual secondary channel The signal after In acoustic space, interference cancels out, thus obtaining residual noise. This enables active noise control.

[0165] Optionally, the system monitors abrupt changes in the secondary channel model or target noise online by calculating the energy change of the residual noise after smoothing and filtering in real time. This, in turn, affects the coefficients of the feedforward controller 11, the feedforward controller 22, the Fourier analyzer 31, the linear prediction filter 42, and the secondary channel estimation model. The coefficients and adjustment gain of the auxiliary noise amplitude adjustment module 52 are reinitialized;

[0166] The energy of the residual noise after smoothing filtering is:

[0167]

[0168] in, Forgetting factor in smoothing filtering;

[0169] exist At any time, according to After performing time averaging and smoothing filtering, we get:

[0170]

[0171] in, Positive integers greater than 1 The time-averaged window length, For a moment, and ;;

[0172] when Always satisfied At that time, The timekeeping system is reinitialized; among them, This is the threshold parameter.

[0173] This invention provides a narrowband front-feedback hybrid active noise control system. An auxiliary filtering subsystem 3 separates narrowband residual noise components related to the reference signal and residual noise components unrelated to the reference signal from the residual noise. A linear predictive filtering subsystem 4 separates narrowband residual noise components and broadband residual noise components unrelated to the reference signal from the residual noise components. The use of auxiliary filtering subsystem 3 and linear predictive filtering subsystem 4 enhances the independence between the feedforward active noise control subsystem 1, the feedback active noise control subsystem 2, and the secondary channel online identification subsystem 5, improving the system's dynamic performance. Simultaneously, an auxiliary noise amplitude adjustment module 52 is introduced to improve the system's noise suppression performance. This invention effectively reduces both narrowband target noise components related to and unrelated to the reference signal, improving noise reduction performance and broadening its application range.

[0174] Example 2:

[0175] This embodiment provides a narrowband front feedback hybrid active noise control method. The method applies a narrowband front feedback hybrid active noise control system provided in this embodiment, and the method includes:

[0176] Step 1: Set initial system values ​​and user parameters;

[0177] Set the update step size for the feedforward controller 11 and the Fourier analyzer 31; set the feedback controller 22, the linear prediction filter 42, and the secondary channel estimation model respectively. The length and step size; setting the order of the delay element 41; setting the forgetting factor of the auxiliary noise amplitude adjustment module 52. Power index The feedforward controller 11, feedback controller 22, Fourier analyzer 31, linear prediction filter 42, and secondary channel estimation model are respectively set. and the adjustment gain of the auxiliary noise amplitude adjustment module 52. The initial values ​​are all zero; auxiliary noise is set. ;

[0178] Step 2: Obtain the reference signal;

[0179] exist At any given time, a narrowband reference frequency is obtained using a non-acoustic sensor. ; Employing internal reference synthesis module 21 and Residual noise at time and Obtain internal reference signal The adjustment gain is obtained by using an auxiliary noise amplitude adjustment module 52. ;

[0180] Step 3: In At any given moment, the feedforward controller 11 first provides a narrowband secondary sound source related to the reference signal. The feedback controller 22 provides a narrowband secondary sound source independent of the reference signal. Secondly, auxiliary noise is obtained using auxiliary noise amplitude adjustment module 52. ,and then , and The three components combined produce a secondary sound source. Finally, residual noise The narrowband residual noise component related to the reference signal is obtained by the auxiliary filtering subsystem 3. and residual noise components independent of the reference signal ;

[0181] Step Four: In At time , residual noise components independent of the reference signal The narrowband residual noise components independent of the reference signal are obtained by the linear predictive filtering subsystem 4. Broadband residual noise components related to auxiliary noise and additive noise in the target signal. ; Used as input for the auxiliary noise amplitude adjustment module in the secondary channel online identification subsystem 5; Used as the expected input for the secondary channel online identification module 51;

[0182] Step 5: Update the control system;

[0183] According to the reference signal and narrowband residual noise components related to the reference signal Calculate and update feedforward controller 11 in The coefficient at time;

[0184] Based on the internally synthesized reference signal and narrowband residual noise components independent of the reference signal The calculation update feedback controller 22 is in The coefficient at time;

[0185] Based on residual noise components independent of the reference signal and broadband residual noise components independent of the reference signal Calculate and update the linear prediction filter 42 in The coefficient at time;

[0186] According to auxiliary noise and The secondary channel estimation model in the online identification module 51 of the secondary channel is calculated and updated. exist The coefficient at time;

[0187] Based on the narrowband residual noise component related to the reference signal and narrowband residual noise components independent of the reference signal The auxiliary noise amplitude adjustment module 52 is calculated and updated. Adjusting the gain at any given moment;

[0188] Step Six: Monitor abrupt changes in the actual secondary channel or target noise;

[0189] Real-time calculation of the change in residual noise energy after smoothing filtering, if it satisfies Then in The coefficients of feedforward controller 11, feedforward controller 22, Fourier analyzer 31, linear prediction filter 42, and secondary channel estimation model at each time step. The coefficients and the adjustment gain of the auxiliary noise amplitude adjustment module 52 are reinitialized, and then proceed to step seven; if the conditions are met... If so, proceed directly to step seven;

[0190] Step 7: Return to Step 2 and repeat Steps 2 through 6 until the system gradually converges and reaches a steady state, thus achieving noise control.

[0191] Example 3: Validation under the condition of target noise containing uncorrelated narrowband components and abrupt changes in secondary channels.

[0192] Let the normalized angular frequencies of the three frequency components of the reference signal be respectively , and Narrowband target noise component related to the reference signal The corresponding discrete Fourier coefficients are respectively , , , , , Narrowband target noise component The frequency is and The corresponding cosine amplitudes are all 1; the additive noise in the narrowband target noise is Gaussian white noise with zero mean and variance of 0.01. To simulate the large abrupt changes in the secondary channel, the actual secondary channel... A linear FIR model with a cutoff frequency of 0.45π is used, with model lengths of 21 and 11 for the first and second halves, respectively; a secondary channel estimation model is also employed. Length 31; auxiliary Gaussian white noise The mean is zero and the variance is 1.0. The feedback controller uses a linear filter with a length of 81. The update step sizes of the feedforward controller, feedback controller, and secondary online identification module are 0.01, 0.0015, and 0.0005, respectively. The delay element has an order of 11; the linear prediction filter uses a linear filter with a length of 41 and an update step size of 0.0005. The initial step size of the Fourier analyzer is 0.01, and the user parameters... The values ​​were 0.99975 and 0.00002 respectively; forgetting factor Power index . , , The values ​​are 0.98, 1.1, and 20 respectively. The number of independent runs is 100; the length of the simulation sampling points is 40,000.

[0193] Figure 2 The figure shown is a dynamic change curve of the residual noise mean square error in Example 3. Figure 3 The figure shown is a dynamic curve of the mean square error of the secondary channel estimation in Example 3. Figure 2 As shown, the system has good dynamic convergence performance. When the system reaches steady state, the steady-state values ​​of the residual noise mean square error of the first half and the second half of the system are 0.0131 and 0.129, respectively. They tend to the variance of the additive white Gaussian noise in the target noise, indicating that the system provided by the present invention has good narrowband target noise suppression performance including unrelated narrowband components. Figure 3 This indicates that the system provided by the present invention can not only effectively estimate the abrupt changes of the secondary channel online, but also has good speed and accuracy in online identification of the secondary channel.

[0194] Example 4: Verification under target noise conditions including relevant narrowband components

[0195] Suppose that the narrowband target noise includes a correlated narrowband component, and the narrowband target noise component... The frequency is and The corresponding cosine amplitude is 1; other parameter settings are the same as in Example 3.

[0196] Figure 4 The figure shown is a dynamic change curve of the residual noise mean square error in Example 4. Figure 5 The figure shown is a dynamic curve of the mean square error of the secondary channel estimation in Example 4. Figure 4As shown, the system still has good dynamic convergence performance in this case, and when the system reaches steady state, the steady-state values ​​of the residual noise mean square error of the first half and the second half of the system are 0.0121 and 0.0119, respectively, which tend to the variance of additive white Gaussian noise in the target noise, further indicating that the system provided by the present invention has good narrowband target noise suppression performance including relevant narrowband components. Figure 5 This indicates that the system provided by the present invention can still effectively estimate the abrupt changes of the secondary channel online in this situation, and also has good speed and accuracy for online identification of the secondary channel.

[0197] The above embodiments three and four, respectively, verify the effectiveness and practicality of the narrowband front feedback hybrid active noise control system and method provided by the present invention, based on two cases: the target noise containing correlated narrowband components and uncorrelated narrowband components, as well as the case of abrupt changes in the actual secondary channel. This will further promote the practical application of active noise control technology. Some steps in the embodiments of the present invention can be implemented in software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or hard disk.

[0198] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 narrowband forward feedback hybrid active noise control system, characterized in that, include: A feedforward active noise control subsystem (1) is used to synthesize a narrowband secondary sound source related to the reference signal; Feedback active noise control subsystem (2) is used to synthesize a narrowband secondary sound source that is independent of the reference signal; The auxiliary filtering subsystem (3) is used to separate the narrowband residual noise component related to the reference signal and the residual noise component unrelated to the reference signal from the residual noise. The linear predictive filtering subsystem (4) is used to separate the narrowband residual noise component and the broadband residual noise component from the residual noise component that is independent of the reference signal. as well as The secondary channel online identification subsystem (5) is used to estimate the time-varying actual secondary channel online, and includes an auxiliary noise amplitude adjustment module (52). The auxiliary filtering subsystem (3) is connected to the feedforward active noise control subsystem (1), the linear prediction filtering subsystem (4), and the secondary channel online identification subsystem (5). The narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem (3) is used as the error output of the feedforward active noise control subsystem (1) and the input of the auxiliary noise amplitude adjustment module (52) of the secondary channel online identification subsystem (5), respectively. At the same time, the residual noise component unrelated to the reference signal separated by the auxiliary filtering subsystem (3) is used as the input of the linear prediction filtering subsystem (4). The linear predictive filtering subsystem (4) is connected to the feedback active noise control subsystem (2) and the secondary channel online identification subsystem (5); the narrowband residual noise component that is independent of the reference signal separated by the linear predictive filtering subsystem (4) is used as the error output of the feedback active noise control subsystem (2) and the input of the auxiliary noise amplitude adjustment module (52) of the secondary channel online identification subsystem (5), respectively; at the same time, the broadband residual noise component separated by the linear predictive filtering subsystem (4) is used as the desired input of the secondary channel online identification subsystem (5); The auxiliary filtering subsystem (3) includes a Fourier analyzer (31) and a variable step-size minimum mean square algorithm module (32); the Fourier analyzer (31) uses discrete Fourier coefficients, i.e. ; The variable step size minimum mean square algorithm module (32) uses the broadband residual noise component that is independent of the reference signal separated by the auxiliary filtering subsystem (3). As an error output, it is used to update the Fourier analyzer (31); the coefficient update formula of the Fourier analyzer (31) is: in, The update step size of the Fourier analyzer (31) is set to a positive value; the variable step size minimum mean square algorithm module (32) is used to update the step size. Real-time updates are performed, and the step size update formula is as follows: in, These are user parameters, and all are positive values ​​less than 1. The narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem (3) is: The residual noise component that is independent of the reference signal is separated by the auxiliary filtering subsystem (3). ； in, for Residual noise at any given moment; Meanwhile, the narrowband residual noise component related to the reference signal separated by the auxiliary filtering subsystem (3) is used as the input of the auxiliary noise amplitude adjustment module (52) in the secondary channel online identification subsystem (5); The secondary channel online identification subsystem (5) also includes a secondary channel online identification module (51). The secondary channel online identification module (51) includes a secondary channel estimation model. The secondary channel online identification module (51) uses... As the desired input, use Gaussian white noise The auxiliary noise generated after passing through the auxiliary noise amplitude adjustment module (52) Using the least mean square algorithm as the reference input, the time-varying actual secondary channel is estimated online. The secondary channel estimation model of the secondary channel online identification module (51) A linear filter is used, with coefficients and length respectively. and The formula for updating its coefficients is: in, This is the update step size for the secondary channel estimation model, and its value is positive. The output of the secondary channel estimation model of the secondary channel online identification module (51); The error output of the secondary channel online identification module (51); The auxiliary noise for: in, The mean is zero and the variance is Additive white Gaussian noise; The adjustment gain of the auxiliary noise amplitude adjustment module (52); Forgetting factor, ; It is the power exponent, with a value of 1 or 2.

2. The narrowband forward feedback hybrid active noise control system according to claim 1, characterized in that, The feedforward active noise control subsystem (1) includes a feedforward controller (11) and a first filter-X minimum mean square algorithm module (12). The feedforward controller (11) is represented by discrete Fourier coefficients, which are: ,in The number of narrowband frequencies of the reference signal; For a moment, ; The first filter-X least mean square algorithm module (12) uses the narrowband residual noise component related to the reference signal separated by the auxiliary filter subsystem (3). As an error output, it is used to update the feedforward controller (11); the coefficient update formula of the feedforward controller (11) is: in, The update step size of the feedforward controller (31) is a positive value; Reference signal The outputs of the secondary channel estimation model in the first filter-X minimum mean square algorithm module (12) are respectively processed; ; The first reference signal A narrowband frequency; The feedforward active noise control subsystem (1) obtains the narrowband secondary sound source related to the reference signal as: 。 3. A narrowband forward feedback hybrid active noise control system according to claim 2, characterized in that, The feedback active noise control subsystem (2) includes an internal reference synthesis module (21), a feedback controller (22), and a second filter-X least mean square algorithm module (23). The internal reference synthesis module (21) includes a secondary channel estimation model and a first-order delay element, used to synthesize the internal reference signal. ,Right now in, The output of the feedback controller (22) is the output of the secondary channel estimation model. The narrowband residual noise component that is independent of the reference signal is separated by the auxiliary filtering subsystem (4); The feedback controller (22) employs a linear filter, the coefficients and length of which are respectively... and ; The second filter-X least mean square algorithm module (23) uses the narrowband residual noise component that is independent of the reference signal separated by the auxiliary filter subsystem (4). As an error output, it is used to update the coefficients of the feedback controller (22); the coefficient update formula of the feedback controller (22) is: in, The update step size of the feedback controller (22) is a positive value; Provided for the internal reference synthesis module (21) The output of the secondary channel estimation model in the second filter-X minimum mean square algorithm module (23); The narrowband secondary sound source obtained by the feedback active noise control subsystem (2), which is independent of the reference signal, is: 。 4. A narrowband forward feedback hybrid active noise control system according to claim 3, characterized in that, The linear prediction filter subsystem (4) includes a delay element (41) and a linear prediction filter (42), which are cascaded. The order of the delay element (41) is... The coefficients and length of the linear prediction filter (42) are respectively and Its coefficients are updated using the least mean square algorithm, that is: in, The update step size of the linear prediction filter (42) is a positive value. The broadband residual noise component separated from the linear prediction subsystem (4); The narrowband residual noise component and the broadband residual noise component, which are independent of the reference signal and are separated from the residual noise by the linear predictive filtering subsystem (4), are respectively... The narrowband residual noise component, which is independent of the reference signal, separated by the linear predictive filtering subsystem (4) is used as the input of the auxiliary noise amplitude adjustment module (52) in the secondary channel online identification subsystem (5); at the same time, the broadband residual noise component separated by the linear predictive filtering subsystem (4) is used as the desired input of the secondary channel online identification subsystem (5).

5. A narrowband forward feedback hybrid active noise control system according to claim 4, characterized in that, The synthesized secondary sound source is: Target noise Secondary sound source provided by secondary speakers Through the actual secondary channel The signal after In acoustic space, interference cancels out, thus obtaining residual noise. To achieve active noise control; Among them, the actual secondary channel This represents the acoustic space model from the secondary loudspeaker to the error microphone; The target noise is: in, Within the acoustic space The reference signal corresponding to each narrowband frequency passes through the actual secondary channel. The signal that propagates to the error microphone; For acoustic space and A narrowband target noise component that is related to or unrelated to a narrowband frequency; The mean is zero and the variance is Additive white Gaussian noise.

6. A narrowband forward feedback hybrid active noise control system according to claim 5, characterized in that, The system monitors abrupt changes in the secondary channel model or target noise online by calculating the energy change of the residual noise after smoothing and filtering in real time, in order to adjust the coefficients of the feedforward controller (11), the feedforward controller (22), the Fourier analyzer (31), the linear prediction filter (42), and the secondary channel estimation model. The coefficients and the adjustment gain of the auxiliary noise amplitude adjustment module (52) are reinitialized; The energy of the residual noise after smoothing filtering is: in, Forgetting factor in smoothing filtering; exist At any time, according to After performing time averaging and smoothing filtering, we get: in, A positive integer greater than 1 The time-averaged window length, For a moment, and ; when Always satisfied At that time, The timekeeping system is reinitialized; among them, This is the threshold parameter.

7. A narrowband forward feedback hybrid active noise control system according to any one of claims 1-6, characterized in that, The active noise control system uses a non-acoustic microphone to acquire the reference signal, an error microphone to acquire the residual noise, and a secondary loudspeaker to provide the secondary sound source; the actual secondary channel in the acoustic space is the channel model from the secondary sound source to the error microphone.

8. A narrowband front feedback hybrid active noise control method, characterized in that, The method applies the narrowband forward feedback hybrid active noise control system described in claim 7, and the method includes: Step 1: Set system parameters; Set the update step size of the feedforward controller (11) and Fourier analyzer (31); set the feedback controller (22), linear prediction filter (42), and secondary channel estimation model respectively. The length and step size; the order of the delay stage (41); the forgetting factor of the auxiliary noise amplitude adjustment module (52). Power index The feedforward controller (11), feedback controller (22), Fourier analyzer (31), linear prediction filter (42), and secondary channel estimation model are respectively set. and the adjustment gain of the auxiliary noise amplitude adjustment module (52). The initial values ​​are all zero; auxiliary noise is set. ; Step 2: Obtain the reference signal; exist At any given time, a narrowband reference frequency is obtained using a non-acoustic sensor. ; using an internal reference synthesis module (21) and Residual noise at time and Obtain internal reference signal The amplitude adjustment gain is obtained by using an auxiliary noise amplitude adjustment module (52). ; Step 3: In At any given moment, the feedforward controller (11) first provides a narrowband secondary sound source related to the reference signal. The feedback controller (22) provides a narrowband secondary sound source independent of the reference signal. Secondly, the auxiliary noise is obtained using the auxiliary noise amplitude adjustment module (52). ,and then , and The three components combined produce a secondary sound source. Finally, residual noise The narrowband residual noise component related to the reference signal is separated by the auxiliary filtering subsystem (3). and residual noise components independent of the reference signal ; Step Four: In At time , the residual noise component independent of the reference signal The narrowband residual noise components independent of the reference signal are obtained by the linear predictive filtering subsystem (4). Broadband residual noise components related to auxiliary noise and additive noise in the target signal. ; Used as input to the auxiliary noise amplitude adjustment module (52) in the secondary channel online identification subsystem (5); Used as the expected input for the secondary channel online identification module (51); Step 5: Update the control system; According to the reference signal and narrowband residual noise components related to the reference signal The feedforward controller (11) is calculated and updated in the following way. The coefficient at time; Based on the internally synthesized reference signal and narrowband residual noise components independent of the reference signal The feedback controller (22) is calculated and updated in the following ways. The coefficient at time; Based on residual noise components independent of the reference signal and broadband residual noise components independent of the reference signal Calculate and update the linear prediction filter (42) in The coefficient at time; According to auxiliary noise and The secondary channel estimation model of the secondary channel online identification module (51) is calculated and updated. exist The coefficient at time; Based on the narrowband residual noise component related to the reference signal and narrowband residual noise components independent of the reference signal The auxiliary noise amplitude adjustment module (52) is calculated and updated in the calculation. Adjusting the gain at any given moment; Step Six: Monitor abrupt changes in the actual secondary channel or target noise; Real-time calculation of the change in residual noise energy after smoothing filtering, if it satisfies Then in The coefficients of the feedforward controller (11), the feedforward controller (22), the Fourier analyzer (31), the linear prediction filter (42), and the secondary channel estimation model at each time step. The coefficients and the adjustment gain of the auxiliary noise amplitude adjustment module (52) are reinitialized, and then proceed to step seven; if the conditions are met... If so, proceed directly to step seven; Step 7: Return to Step 2 and repeat Steps 2 through 6 until the system gradually converges and reaches a steady state, thus achieving noise control.

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