CN113327570B - Narrow-band feedforward type active noise control system and method - Google Patents
Narrow-band feedforward type active noise control system and method Download PDFInfo
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Abstract
The invention provides a narrow-band feedforward type active noise control system and a method, wherein the system comprises a reference signal synthesis subsystem, an on-line identification subsystem of an acoustic feedback channel, a secondary sound source synthesis subsystem, an on-line identification subsystem of a secondary channel and a residual noise separation subsystem; the broadband component separated by the residual noise separation subsystem is used as the expected input of the secondary channel online identification module, so that the influence of the narrowband component in the residual noise on the online identification performance of the secondary channel is reduced; the narrow-band component separated by the residual noise separation subsystem is used as the error output of the least mean square algorithm module, so that the independence between the secondary sound source synthesis subsystem and the secondary channel on-line identification subsystem is improved; and the sum of the separated narrow-band components is used as the input of the auxiliary noise amplitude adjustment module, the generated colored noise is used as the reference input of the on-line identification subsystem of the acoustic feedback channel, the on-line identification performance of the acoustic feedback channel is improved, and the influence of the acoustic feedback is compensated.
Description
Technical Field
The invention relates to a narrow-band feedforward type active noise control system and a method, and belongs to the technical field of active noise control. In particular, the invention relates to a narrow-band feedforward type active noise control system and method based on acoustic feedback and secondary channel on-line identification.
Background
In view of the adverse effects of hearing health deterioration, production safety hazards and the like caused by industrial noise pollution, development of effective noise suppression technologies is showing urgency. The traditional passive noise control technology mainly comprises sound absorption, sound insulation, sound barrier and the like, has good high-frequency noise inhibition performance, and can effectively reduce high-frequency noise in a wider frequency band range, but the inhibition performance on low-frequency noise is insufficient due to the restriction of factors such as the volume, cost and the like of a control device. In contrast, the active noise control technique utilizes the destructive interference principle of sound waves and has good low-frequency noise suppression performance. The technology has the advantages of small volume, low cost and the like, is suitable for controlling low-frequency harmonic noise and noise in an audio frequency range, and is an advantageous supplement to the traditional passive noise control technology (S.M. Kuo and D.R. Morgan, "Active noise control: a tutorial review," Proc.IEEE, vol.87, no.6, pp.943-973, jun.1999.).
In actual working conditions, a large amount of periodic harmful noise (such as fan noise, cutter noise and the like) generated by rotating equipment exists, a narrow-band component of the periodic harmful noise is dominant, and when a traditional narrow-band feedforward ANC system acquires a reference signal by adopting a non-acoustic sensor (such as a tachometer and the like), the problem of frequency imbalance possibly exists, so that the noise reduction performance of a control system is seriously influenced. If the active noise control system adopts the acoustic sensor to acquire the reference signal, the problem of frequency offset possibly existing in the non-acoustic sensor can be effectively avoided, but acoustic feedback is introduced at the same time. Therefore, the development of a high-performance narrow-band feedforward ANC system containing acoustic feedback has important practical application value.
Many scholars at home and abroad have conducted a great deal of research work around the structural and algorithmic optimization of the narrow-band feedforward ANC system with acoustic feedback. The conventional method for identifying the acoustic feedback channel usually adopts a prediction filter with a larger length to reduce the influence of reference noise on the on-line identification precision of the acoustic feedback channel, and usually adopts signals related to the estimation of the acoustic feedback channel and the prediction filter to carry out the amplitude adjustment of auxiliary noise, which has the defects of local optimization and high calculation cost (S.Ahmed, M.T.Akhtar.Gain scheduling of auxiliary noise and variable step-size for online acoustic feedback cancellation in narrowband active noise control systems [ J ]. IEEE Transactions on Audio, spech, and Language Processing,2017,25 (2): 333-343). The traditional secondary channel online identification method usually adopts a self-adaptive linear enhancer or a predictive filter to reduce the input of a narrow-band component in residual noise to a secondary channel identification link so as to improve the independence between a controller and the secondary channel online identification link, but has the defects of poor separation performance of residual errors, higher calculation cost and low operation efficiency, and is difficult to ensure the independence between the controller and the secondary channel online identification link, thereby influencing the convergence performance of a system and being unfavorable for noise reduction application in the case of strong non-stationary noise (C.Y.Chang, S.M.Kuo, C.W.Huang.Secondary path modeling for narrowband active noise control systems [ J ]. Applied Acoustics,2018, 131:154-164.).
In 2019, xiao et al developed a multi-channel active noise control system based on acoustic feedback and synchronous on-line recognition of secondary channels, which can effectively overcome the influence of acoustic feedback, and can cope with the time-varying properties of secondary channels, and reduce residual noise energy (T.Bai, Z.Wang, Y.Xiao, Y.Ma, L.Ma, and k.khoraani.a multi-channel narrowband active noise control system with simultaneous online secondary-and feedback-path modeling.ieee APCCAS, pp.289-292, 2019.). However, the problems of the conventional active noise control system based on acoustic feedback and on-line recognition of the secondary channel mainly include: 1) The self-adaptive linear enhancer or the predictive filter is adopted for the on-line identification of the acoustic feedback channel and the secondary channel identification links, so that the calculation cost is high and the operation efficiency is low; 2) The system directly adopts a function expression related to the residual noise to adjust the auxiliary noise, so that the auxiliary noise is used as a reference input for an on-line synchronous identification link of acoustic feedback and a secondary channel, and the auxiliary noise still has a large contribution to the residual noise after the system reaches a steady state, so that the overall noise reduction performance of the system is severely restricted; 3) The system directly adopts residual noise, and on one hand, the residual noise is used as expected input of a secondary channel on-line identification link, and the expected input is influenced due to the fact that the residual noise contains a narrow-band component; on the other hand as an error input for a narrowband controller, which will be affected by broadband white noise in the residual noise; the two aspects can lead to poor independence between the narrowband controller and the secondary channel on-line identification link, and influence the convergence performance and the noise reduction speed of the system.
Disclosure of Invention
In order to solve the problems, the invention provides a more effective and practical narrow-band feedforward type active noise control system and method based on acoustic feedback and secondary channel on-line identification. Specifically, the invention provides a narrow-band feedforward type active noise control system and a narrow-band feedforward type active noise control method based on acoustic feedback and secondary channel on-line identification, and the technical scheme is as follows.
The invention provides a narrow-band feedforward type active noise control system based on acoustic feedback and secondary channel on-line identification, which comprises a reference signal synthesis subsystem (1), an acoustic feedback channel on-line identification subsystem (2), a secondary sound source synthesis subsystem (3), a secondary channel on-line identification subsystem (4) and a residual noise separation subsystem (5); the reference signal synthesis subsystem (1) comprises a serial adaptive band-pass filter, is used for synthesizing a broadband reference component and a narrowband reference component, uses the broadband reference component as error output of the acoustic feedback channel on-line identification subsystem (2), and uses the narrowband reference component as reference input of the secondary sound source synthesis subsystem (3); the on-line identification subsystem (2) of the acoustic feedback channel utilizes a least mean square algorithm to estimate an acoustic feedback channel model (21) in an on-line mode and is used for compensating acoustic feedback; a secondary sound source synthesis subsystem (3) for synthesizing a secondary sound source; the secondary sound source synthesis subsystem (3) comprises a narrowband controller (31) and a filtering-X least mean square algorithm module (32) with first-order delay; the secondary channel online identification subsystem (4) comprises a secondary channel online identification module (41) and an auxiliary noise amplitude adjustment module (42); the secondary channel online identification subsystem (4) utilizes a secondary channel online identification module (41) to carry out real-time online estimation on an actual secondary channel, and an obtained secondary channel estimation model is used as a filtering link of a filtering-X least mean square algorithm module (32) in the secondary sound source synthesis subsystem (3); the residual noise separation subsystem (5) includes a parallel bandpass filter for separating wideband and narrowband components from residual noise, the wideband component being used as a desired input to a secondary channel on-line recognition module (41), the narrowband component being used as an input to an auxiliary noise amplitude adjustment module (42) and an error output of a filtered-X least mean square algorithm module (32) of the narrowband controller, respectively.
The invention also provides a narrow-band feedforward type active noise control method based on the on-line identification of the acoustic feedback and the secondary channel, which adopts the narrow-band type active noise control system based on the on-line identification of the acoustic feedback and the secondary channel, and comprises the following steps:
step one: the secondary sound source synthesis subsystem (3) synthesizes the secondary sound source narrowband component and simultaneously generates auxiliary noise v with the auxiliary noise amplitude adjustment module (42) 0 (n) adding to synthesize a secondary sound source y (n);
step two: the secondary sound source y (n) generates an actual sound feedback signal y through an actual sound feedback channel f (n); actual reference signal x s (n) subtracting the actual acoustic feedback signal y f (n) obtaining a reference signal x r (n); reference signal x r (n) subtracting the acoustic feedback channel estimation model (21)Signals derived from acoustic feedback signals generated
As input to a reference signal synthesis subsystem (1); the reference signal synthesis subsystem (1) synthesizes a narrowband reference component and a wideband reference component respectively; at the same time, the secondary sound source y (n) generates anti-noise y through the actual secondary channel p (n); target noise p (n) minus anti-noise y p (n) obtaining residual noise e (n); at the same time, auxiliary noise v generated by the auxiliary noise amplitude adjustment module (42) 0 (n) serving as a reference input to the secondary channel on-line recognition module (41);
step three: the residual noise e (n) is separated into broadband separation d by a residual error difference ion system (5) s (n) and narrowband component u i (n)；
Step four: a coefficient update iteration related to the center frequency of the IIR trap in the reference signal synthesis subsystem (1); meanwhile, updating and iterating coefficients of an acoustic feedback channel estimation model (21) in the acoustic feedback channel online identification subsystem (2) by using an LMS algorithm; meanwhile, the coefficients of a secondary channel estimation model (41) in the secondary channel online identification subsystem (4) are updated and iterated by using an LMS algorithm; meanwhile, the coefficients of the narrowband controller (31) in the secondary sound source synthesis subsystem (3) are updated and iterated by using a filtering-X least mean square algorithm module (32) with first-order delay;
step five: returning to the first step, repeating the first step to the fourth step until the system converges and reaches a steady state.
In one embodiment of the invention, in the secondary sound source synthesis subsystem (3), a narrowband controller (31) is used for synthesizing secondary sound source narrowband components into
Wherein the narrowband controller coefficients
The initial values of (1) are zero, and the initial input of the narrowband controller (31) is zero; and further synthesizing to obtain a secondary sound source y (n) =y 0 (n-1)+v 0 (n)，Wherein v is 0 And (n) is colored noise output by the auxiliary noise amplitude adjustment module (42).
In one embodiment of the invention, the output of the secondary sound source y (n) through the actual secondary channel model is
Finally, the system residual noise is obtained as e (n) =p (n) -y p (n). The actual acoustic feedback path F (z) is also represented by the FIR model with coefficients +.>
Length M f . The output of the secondary sound source y (n) through the actual acoustic feedback channel model is +.>
In one embodiment of the invention, the acoustic feedback channel on-line recognition subsystem (2) utilizes colored noise v obtained by an auxiliary noise amplitude adjustment module (42) 0 And (n) the model is used as a reference input of a least mean square algorithm in the on-line recognition subsystem (2) of the acoustic feedback channel to finish the model estimation of the acoustic feedback channel so as to compensate the acoustic feedback. Secondary sound source y (n) through acoustic feedback channel estimation model
The output of (2) is +.>
In one embodiment of the invention, the residual noise separation subsystem (5) is a band-pass filter bank consisting of q band-pass filters in parallel, each band-pass filter being composed of a second order IIR trap, wherein the z-domain model of the ith second order IIR trap is
Wherein ρ is the polar radiusParameters, the values of which are between 0 and 1; c i ＝-2cos(ω i ) Is a coefficient related to the center frequency of the second order IIR trap, is determined by a reference signal synthesis subsystem (1). The z-domain model of the corresponding ith bandpass filter is:
in one embodiment of the invention, the residual noise separation subsystem (5) is used for separating the broadband component d from the residual noise e (n) s (n) and narrowband component
Wherein the broadband component d s (n) used as the desired input of the secondary channel on-line recognition module (41), narrowband component +.>
Error output of a filter-Xleast mean square algorithm module (32) for use as a narrowband controller, q narrowband components +.>
The sum is used as an input to an auxiliary noise amplitude adjustment module (42) to promote independence between the secondary sound source synthesis subsystem (3) and the secondary channel on-line recognition subsystem (4).
In one embodiment of the invention, the secondary channel on-line identification module (41) adopts the broadband component d separated by the residual noise separation subsystem (5) s (n) as expected input, improving the performance of the secondary channel online identification subsystem, and reducing the influence of the narrowband component in the residual noise on the performance of the secondary channel online identification subsystem; the auxiliary noise amplitude adjustment module (42) is the sum u of q narrow-band components separated from the residual noise separation subsystem (5) b The relational expression of (n) is expressed as:
f[u b (n)]＝αf[u b (n-1)]+(1-α)|u b (n-1)| λ ，λ＝1,2,3,4
in the method, in the process of the invention,alpha is a convergence factor, and the value of alpha is between 0 and 1; q is the number of narrowband frequency channels; n is the moment, n is more than or equal to 0; f [ u ] b (n)]For a mean value of zero and a variance of
Amplitude adjustment is performed on the Gaussian white noise v (n), and the obtained colored noise is expressed as v 0 (n)＝f[u b (n)]v(n)。
In one embodiment of the invention, the secondary channel is in the on-line identification subsystem (4), and the narrow-band component separated by the residual noise separation subsystem (5)
The sum is used as an input to an auxiliary noise amplitude adjustment module (42), and the generated colored noise v 0 And (n) synchronously serving as a reference input of the on-line identification subsystem (2) of the acoustic feedback channel and a reference input of the on-line identification module (41) of the secondary channel respectively, and effectively reducing the contribution of auxiliary noise to residual noise while ensuring the on-line identification performance of the acoustic feedback channel and the on-line identification performance of the secondary channel respectively, so that the overall noise reduction performance of the system tends to an ideal level.
In one embodiment of the invention, the filter-Xleast mean square algorithm module (32) with first order delay adopts the ith narrowband component u separated by the residual noise separation subsystem (5) i (n) as an error output, the corresponding updated formula for the narrowband controller coefficients is:
wherein mu is h Updating step length for the narrow-band controller coefficient;
output for reference signal synthesis subsystem (1)Reference signal +.>
Via a secondary channel estimation model->
The filtered signal.
In one embodiment of the invention, the acoustic feedback channel estimation model (21) in the acoustic feedback channel on-line recognition subsystem (2) is updated by using a least mean square algorithm, and the coefficients are as follows
For the model length, the coefficient update formula is:
wherein mu is f For the update step of the acoustic feedback channel estimation model (21), usually a positive value smaller than 1 is taken.
In one embodiment of the invention, the colored noise v output by the auxiliary noise amplitude adjustment module (42) 0 (n) respectively inputting the color noise v into an actual secondary channel and a secondary channel on-line identification module (41) 0 (n) a contribution to residual noise. Updating a secondary channel estimation model in a secondary channel on-line recognition module (41) by using a least mean square algorithm, wherein the coefficients are as follows
For the model length, the coefficient update formula is: />
Wherein mu is s For the update step of the secondary channel on-line identification module (41), normally taking a positive value smaller than 1; e, e s (n)(＝d s (n)+y s (n)) is the error of the secondary channel on-line identification module (41)And (5) outputting a difference.
The invention has the beneficial effects that:
1. the invention uses the broadband component separated by the residual noise separation subsystem (5) as the expected input of the secondary channel on-line identification module (41), reduces the influence of the narrowband component in the residual noise on the performance of the secondary channel on-line identification subsystem, improves the performance of the secondary channel on-line identification subsystem (4), and improves the stability of the whole system;
2. the invention utilizes the narrow-band component separated by the residual noise separation subsystem (5) to be used as the error output of the filtering-X least mean square algorithm module (32) with first-order delay, improves the independence between the secondary sound source synthesis subsystem (3) and the secondary channel on-line identification subsystem (4), and improves the convergence of the whole system and the performance of coping with strong non-stationary noise;
3. the invention utilizes the narrow-band component separated by the residual noise separation subsystem (5)
And, as input to an auxiliary noise amplitude adjustment module (42), the generated colored noise v 0 (n) the input is used as a reference input of the on-line identification subsystem (2) of the acoustic feedback channel, so that the performance of the on-line identification subsystem (2) of the acoustic feedback channel is integrally improved, and the influence of acoustic feedback is effectively compensated;
4. the invention utilizes the narrow-band component separated by the residual noise separation subsystem (5)
And, as input to an auxiliary noise amplitude adjustment module (42), the generated colored noise v 0 (n) is auxiliary noise, and is further used as reference input of the secondary channel on-line identification module (41), so that the introduced auxiliary noise v is effectively reduced 0 (n) the contribution to the residual noise, so that the overall noise reduction performance of the system tends to an ideal level.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a narrow-band active noise control system based on acoustic feedback and secondary channel on-line recognition according to the first embodiment;
fig. 2 (a) is a system residual noise mean square error data diagram in the second embodiment;
fig. 2 (b) is an estimated mean square error data plot of the acoustic feedback path and secondary path of embodiment two;
FIG. 2 (c) is a graph of the dynamic variation of the frequency dependent coefficients of the narrowband reference component in embodiment two;
fig. 3 (a) is a system residual noise mean square error data diagram of the third embodiment;
FIG. 3 (b) is a graph of the dynamic variation of the frequency dependent coefficients of the narrowband reference component of embodiment three;
wherein: the system comprises a reference signal synthesis subsystem, a 2-sound feedback channel online identification subsystem, a 3-secondary sound source synthesis subsystem, a 4-secondary channel online identification subsystem, a 5-residual noise separation subsystem, a 21-sound feedback channel estimation model, a 31-narrowband controller, a 32-filter-X least mean square algorithm module with first-order delay, a 41-secondary channel online identification module and a 42-auxiliary noise amplitude adjustment module.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
Embodiment one:
the embodiment provides a narrow-band feedforward type active noise control system based on acoustic feedback and secondary channel on-line identification, referring to fig. 1, the active noise control system comprises a reference signal synthesis subsystem (1), an acoustic feedback channel on-line identification subsystem (2), a secondary sound source synthesis subsystem (3), a secondary channel on-line identification subsystem (4) and a residual noise separation subsystem (5); the reference signal synthesis subsystem (1) comprises a serial adaptive band-pass filter, is used for synthesizing a broadband reference component and a narrowband reference component, and is used as an error output of the acoustic feedback channel on-line identification subsystem (2) and a reference input of the secondary sound source synthesis subsystem (3) respectively; the on-line identification subsystem (2) of the acoustic feedback channel utilizes a least mean square algorithm to estimate an acoustic feedback channel model (21) in an on-line mode and is used for compensating acoustic feedback; a secondary sound source synthesis subsystem (3) for synthesizing a secondary sound source; the secondary sound source synthesis subsystem (3) comprises a narrowband controller (31) and a filtering-X least mean square algorithm module (32) with first-order delay; the secondary channel online identification subsystem (4) comprises a secondary channel online identification module (41) and an auxiliary noise amplitude adjustment module (42); the secondary channel online identification subsystem (4) utilizes a secondary channel online identification module (41) to carry out real-time online estimation on an actual secondary channel, and an obtained secondary channel estimation model is used as a filtering link of a filtering-X least mean square algorithm module (32) in the secondary sound source synthesis subsystem (3); the residual noise separation subsystem (5) includes a parallel bandpass filter for separating wideband and narrowband components from residual noise, the wideband component being used as a desired input to a secondary channel on-line recognition module (41), the narrowband component being used as an input to an auxiliary noise amplitude adjustment module (42) and an error output of a filtered-X least mean square algorithm module (32) of the narrowband controller, respectively.
The invention also provides a narrow-band feedforward type active noise control method based on acoustic feedback and secondary channel on-line identification, which comprises the following steps:
step one: the secondary sound source synthesis subsystem (3) synthesizes the secondary sound source narrowband component and simultaneously generates auxiliary noise v with the auxiliary noise amplitude adjustment module (42) 0 (n) adding to synthesize a secondary sound source y (n);
step two: the secondary sound source y (n) generates an actual sound feedback signal y through an actual sound feedback channel f (n); actual reference signal x s (n) subtracting the actual acoustic feedback signal y f (n) obtaining a reference signal x r (n); reference signal x r (n) subtracting the acoustic feedback signal generated by the acoustic feedback channel estimation model (21)Signal signal
As input to a reference signal synthesis subsystem (1); the reference signal synthesis subsystem (1) synthesizes a narrowband reference component and a wideband reference component respectively; at the same time, the secondary sound source y (n) generates anti-noise y through the actual secondary channel p (n); target noise p (n) minus anti-noise y p (n) obtaining residual noise e (n); at the same time, auxiliary noise v generated by the auxiliary noise amplitude adjustment module (42) 0 (n) serving as a reference input to the secondary channel on-line recognition module (41);
step three: the residual noise e (n) is separated into broadband separation d by a residual error difference ion system (5) s (n) and narrowband component u i (n)；
Step four: a coefficient update iteration related to the center frequency of the IIR trap in the reference signal synthesis subsystem (1); meanwhile, updating and iterating coefficients of an acoustic feedback channel estimation model (21) in the acoustic feedback channel online identification subsystem (2) by using an LMS algorithm; meanwhile, the coefficients of a secondary channel estimation model (41) in the secondary channel online identification subsystem (4) are updated and iterated by using an LMS algorithm; meanwhile, the coefficients of the narrowband controller (31) in the secondary sound source synthesis subsystem (3) are updated and iterated by using a filtering-X least mean square algorithm module (32) with first-order delay;
step five: returning to the first step, repeating the first step to the fourth step until the system converges and reaches stability.
Fig. 1 is a schematic diagram of a narrow-band feedforward active noise control system based on acoustic feedback and on-line recognition of secondary channels according to the present embodiment.
The reference signal is
Where q is the number of narrowband components in the reference signal, ">
Is the amplitude of the narrowband component; x is x s (n) zero mean and variance +.>
Additive white gaussian noise of (2); omega s , i Is the frequency of the i-th narrowband component in the reference signal; n is the time (n.gtoreq.0). Reference signal x s (n) through the actual primary channel P (z) (the FIR model coefficients are +.>
Length M p ) The target noise obtained is p (n) =p s (n)+v p (n) wherein v p (n) mean zero, variance +.>
Is a gaussian white noise of (c). The actual secondary channel S (z) is represented by a FIR model with coefficients +.>
Length M s 。
In the secondary sound source synthesis subsystem (3), a narrowband controller (31) is used for synthesizing the narrowband components of the secondary sound source into
Wherein the narrowband controller coefficient->
The initial values of (1) are zero, and the initial input of the narrowband controller (31) is zero; and further synthesizing to obtain a secondary sound source y (n) =y 0 (n-1)+v 0 (n) wherein v 0 And (n) is colored noise output by the auxiliary noise amplitude adjustment module (42).
The output of the secondary sound source y (n) through the actual secondary channel model is
Finally, the system residual noise is obtained as e (n) =p (n) -y p (n). The actual acoustic feedback path F (z) is also represented by an FIR model, with coefficients of
Length M f . The output of the secondary sound source y (n) through the actual acoustic feedback channel model is +.>
Colored noise v output by the auxiliary noise amplitude adjustment module (42) 0 (n) respectively inputting the color noise v into an actual secondary channel and a secondary channel on-line identification module (41) 0 (n) a contribution to residual noise.
An on-line identification subsystem (2) of an acoustic feedback channel, which utilizes colored noise v obtained by an auxiliary noise amplitude adjustment module (42) 0 And (n) the model is used as a reference input of a least mean square algorithm in the on-line recognition subsystem (2) of the acoustic feedback channel to finish the model estimation of the acoustic feedback channel so as to compensate the acoustic feedback. Secondary sound source y (n) through acoustic feedback channel estimation model
The output of (2) is
The reference signal synthesis subsystem (1) comprises a string-type adaptive bandpass filter for synthesizing the wideband reference component and the narrowband reference component. Signals compensated by acoustic feedback
Is used as an input to a string of adaptive bandpass filters, each of which has a model that corresponds to the model of the bandpass filter in the residual noise separation subsystem (5). The output of the ith IIR trap is:
z i (n)＝-ρc i (n)z i (n-1)-ρ 2 z i (n-2)+z i-1 (n)+c i (n)z i-1 (n-1)+z i-1 (n-2)]，i>1
wherein, c i (n) is a coefficient related to the center frequency of the ith IIR trap, and converges to-2 cos ω in steady state s,i The update formula is as follows:
g i (n)＝-ρz i (n-1)+z i-1 (n-1)，i>1
wherein mu is c C is i An update step size of (n); epsilon is a parameter ensuring that the denominator is non-zero; beta is a convergence factor, which takes a value between 0 and 1. The reference signal synthesis subsystem (1) synthesizes the narrowband reference component into
Wherein the method comprises the steps of
The reference signal synthesis subsystem (1) synthesizes a broadband reference component z q (n)。
In the secondary channel on-line identification subsystem (4), the narrow-band component separated by the residual noise separation subsystem (5)
The sum is used as an input to an auxiliary noise amplitude adjustment module (42), and the generated colored noise v 0 (n) synchronously using as the reference input of the acoustic feedback channel on-line identification subsystem (2) and the reference input of the secondary channel on-line identification module (41), respectively, while ensuring on-line identification of the acoustic feedback channel and on-line identification performance of the secondary channel, respectivelyMeanwhile, the contribution of auxiliary noise to residual noise is effectively reduced, and the overall noise reduction performance of the system tends to an ideal level.
The residual noise separation subsystem (5) is a band-pass filter group formed by q band-pass filters in a parallel mode, each band-pass filter is formed by a second-order IIR trap, wherein the z-domain model of the ith second-order IIR trap is as follows:
wherein ρ is a polar radius parameter having a value between 0 and 1; c i ＝-2cos(ω i ) Is a coefficient related to the center frequency of the second order IIR trap, is determined by a reference signal synthesis subsystem (1). The z-domain model of the corresponding ith bandpass filter is:
the residual noise separation subsystem (5) is used for separating broadband components d from residual noise e (n) s (n) and narrowband component
Namely:
u i (n)＝-ρu i (n-1)-ρ 2 u i (n-2)-(1-ρ)[c i (n)e(n-1)+(1+ρ)e(n-2)]
wherein the broadband component d s (n) a narrowband component used as a desired input to the secondary channel on-line recognition module (41)
Error output of a filter-X least mean square algorithm module (32) used as a narrowband controller, q narrowband components
Sum u b (n) is used as an input to the auxiliary noise amplitude adjustment module (42) to promote independence between the secondary sound source synthesis subsystem (3) and the secondary channel on-line recognition subsystem (4).
The secondary channel on-line identification module (41) adopts the broadband component d separated by the residual noise separation subsystem (5) s (n) as expected input, improving the performance of online identification of the secondary channel, and reducing the influence of narrowband components in residual noise on the performance of an online identification subsystem of the secondary channel; the auxiliary noise amplitude adjustment module (42) is the sum u of q narrow-band components separated from the residual noise separation subsystem (5) b The relational expression of (n) is expressed as:
f[u b (n)]＝αf[u b (n-1)]+(1-α)|u b (n-1)| λ λ=1 or 2
Wherein, alpha is a convergence factor, and the value of alpha is between 0 and 1; f [ u ] b (n)]For a mean value of zero and a variance of
Amplitude adjustment is performed on the Gaussian white noise v (n), and the obtained colored noise is expressed as v 0 (n)＝f[u b (n)]v(n)。
The filter-X least mean square algorithm module (32) with first-order delay adopts the ith narrow-band component u separated by the residual noise separation subsystem (5) i (n) as an error output, corresponding narrowband controller coefficients
The updated formula of (2) is:
wherein mu is h Updating step length for the narrow-band controller coefficient;
reference signal +.>
Via a secondary channel estimation model->
The filtered signal.
Updating a secondary channel estimation model in a secondary channel on-line recognition module (41) by using a least mean square algorithm, wherein the coefficients are as follows
Length of->
The coefficient update formula is:
wherein mu is s For the update step of the secondary channel on-line identification module (41), normally taking a positive value smaller than 1; e, e s (n)(＝d s (n)+y s (n)) is the error output of the secondary channel on-line identification module (41).
For the secondary sound source y (n) via a secondary channel estimation model +.>
Is provided.
Updating an acoustic feedback channel estimation model (21) in an acoustic feedback channel on-line identification subsystem (2) by using a least mean square algorithm, wherein the coefficient is
For the model length, the coefficient update formula is:
wherein mu is f For the update step of the acoustic feedback channel estimation model (21), usually a positive value smaller than 1 is taken.
The invention is verified to have good active noise control effect by combining two conditions of the simulation secondary channel and the actual secondary channel.
Embodiment two: theoretical verification in case of emulated secondary channel
The reference signal comprises five frequency components and additive Gaussian white noise, wherein the five frequencies of the narrow-band components are 100 Hz, 150 Hz, 300 Hz, 400 Hz and 450Hz respectively, and the sampling rate is 2kHz; the corresponding five frequency components are all of the amplitude
Additive white gaussian noise v s The variance of (n) is 0.0001. Additive white gaussian noise v in target noise p (n) p The variance of (n) is 0.1, and the variance of the auxiliary white gaussian noise for the synchronous on-line recognition of the acoustic feedback and the secondary channel is 1.0. The actual primary channel adopts an FIR model, and the length and the cut-off frequency of the actual primary channel are respectively 48 and 0.4 pi; the actual secondary channel adopts an FIR model, and the length and the cut-off frequency of the actual secondary channel are respectively 21 and 0.4 pi; the actual acoustic feedback channel adopts an FIR model, and the length and the cut-off frequency of the actual acoustic feedback channel are respectively 32 and 0.4pi; the secondary channel FIR estimation model length is 31; the acoustic feedback channel FIR estimation model length is 42; the update step size of the narrowband controller is 0.0025; the update step length of the online identification of the secondary channel is 0.001; the update step length of the on-line identification of the acoustic feedback channel is 0.001; polar radius parameter ρ=0.975; the update step length of the coefficient related to the center frequency is 0.001; alpha, lambda, beta, epsilon are 0.999, 2, 0.98 and 0.01, respectively; the simulation data length is 50000; the number of runs was 40. The initial value of the coefficient related to the center frequency of each IIR trap is set to zero.
As shown in fig. 2 (a), 2 (b) and 2 (c), as shown in the graphs of the mean square error of the system residual noise, the estimated mean square error of the acoustic feedback channel and the secondary channel, and the dynamic change of the coefficient related to the frequency of the narrowband reference component in the case of the simulation noise and the secondary channel in the present embodiment, after the system reaches steady state, the mean square error of the system residual noise is about 0.1054, and the value approaches to the variance of the additive gaussian white noise of the target noise, which indicates that the active noise control system of the present invention has good noise suppression performance; the dynamic curve of the estimated mean square error of the acoustic feedback channel and the secondary channel shows that the active noise control system has good on-line identification performance of the acoustic feedback channel and the secondary channel; from the dynamic curve of the coefficients related to the frequency of the narrowband reference component, it is known that the active noise control system of the present invention has good narrowband reference component synthesis performance.
Embodiment III: experimental verification in the case of an actual secondary channel
The actual secondary channel model is an IIR model (S.M. Kuo and D.R. Morgan. Active noise control systems-algorithms and DSP Implementation, new York: wiley, 1996.). The secondary channel estimation model length is 32; the update step length of the narrowband controller is 0.001; the update step size of the coefficient related to the center frequency is 0.0025; alpha and epsilon are 0.9995 and 0.03, respectively; other experimental conditions and user parameters were the same as in the examples.
As shown in fig. 3 (a) and 3 (b), the dynamic change graphs of the system residual noise mean square error and the coefficient related to the frequency of the narrowband reference component in the actual secondary channel case of the present embodiment can be known: after the system reaches a steady state, the mean square error of the residual noise of the system is about 0.1023, and the value approaches to the variance of the additive Gaussian white noise of the target noise, which shows that the system still has good noise suppression performance under the condition of an actual secondary channel, and indirectly reflects the on-line identification of the sound feedback channel and the on-line identification performance of the secondary channel; from the dynamic curve of the coefficients related to the frequencies of the narrowband reference components, it can be seen that the active noise control system of the present invention is capable of effectively synthesizing the narrowband reference components, providing accurate input for the synthesis of the secondary sound source.
The effectiveness and the practicability of the narrow-band active noise control based on the acoustic feedback and the on-line identification of the secondary channel provided by the invention are jointly verified from two situations of theory and experiment respectively in the embodiment 2 and the embodiment 3, and the practical application process of the active noise control technology is promoted.
Some steps in the embodiments of the present invention may be implemented by using software, and the corresponding software program may be stored in a readable storage medium, such as an optical disc or a hard disk.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (8)
1. A narrow-band feedforward type active noise control system, the active noise control system comprising: the system comprises a reference signal synthesis subsystem (1), an acoustic feedback channel online identification subsystem (2), a secondary sound source synthesis subsystem (3), a secondary channel online identification subsystem (4) and a residual noise separation subsystem (5);
the reference signal synthesis subsystem (1) comprises a string-type adaptive bandpass filter for synthesizing a wideband reference component and a narrowband reference component, and uses the wideband reference component as an error output of the acoustic feedback channel on-line recognition subsystem (2) and the narrowband reference component as a reference input of a secondary acoustic source synthesis subsystem (3);
the acoustic feedback channel online identification subsystem (2) adopts a least mean square algorithm to estimate an acoustic feedback channel model (21) in an online mode, and the acoustic feedback channel model (21) is used for compensating acoustic feedback;
the secondary sound source synthesis subsystem (3) is used for synthesizing a secondary sound source, and the secondary sound source synthesis subsystem (3) comprises a narrow-band controller (31) and a filtering-X least mean square algorithm module (32) with first-order delay;
the secondary channel online identification subsystem (4) comprises a secondary channel online identification module (41) and an auxiliary noise amplitude adjustment module (42); the secondary channel online identification subsystem (4) utilizes the secondary channel online identification module (41) to carry out real-time online estimation on an actual secondary channel, and uses an obtained secondary channel estimation model as a filtering link of the filtering-X least mean square algorithm module (32) in the secondary sound source synthesis subsystem (3);
the residual noise separation subsystem (5) comprises a parallel bandpass filter for separating a wideband component and a narrowband component from residual noise and using the wideband component therein as a desired input to the secondary channel on-line recognition module (41) and the narrowband component therein as an input to the auxiliary noise amplitude adjustment module (42) and an error output of the filter-X least mean square algorithm module (32) of the narrowband controller (31), respectively.
2. Active noise control system according to claim 1, characterized in that the residual noise separation subsystem (5) is adapted to separate a wideband component d from residual noise e (n) s (n) and narrowband component
Wherein the broadband component d s (n) as a desired input to the secondary channel on-line identification module (41), enhancing performance of the secondary channel on-line identification subsystem (4), reducing the impact of narrowband components in the residual noise on performance of the secondary channel on-line identification subsystem (4).
3. The active noise control system of claim 2 wherein the narrowband component is
Error output of a filter-X least mean square algorithm module (32) used as a secondary sound source synthesis subsystem (3) to divide q narrowband components
Sum used as the secondary channel on-line identificationAn auxiliary noise amplitude adjustment module (42) of the subsystem (4) is input to promote independence between the secondary sound source synthesis subsystem (3) and the secondary channel on-line identification subsystem (4).
4. Active noise control system according to claim 1, characterized in that the auxiliary noise amplitude adjustment module (42) outputs colored noise v 0 (n) input to the actual secondary channel and secondary channel on-line recognition module (41) respectively to reduce the colored noise v 0 (n) a contribution to residual noise.
5. Active noise control system according to claim 1, characterized in that the acoustic feedback channel on-line recognition subsystem (2) is configured to compare the colored noise v obtained by the auxiliary noise amplitude adjustment module (42) 0 And (n) the method is used as a reference input of a least mean square algorithm in the on-line recognition subsystem (2) of the acoustic feedback channel, so that the estimation of the acoustic feedback channel model (21) is completed, and the acoustic feedback is further compensated.
6. Active noise control system according to claim 1, characterized in that the narrow-band component separated by the residual noise separation subsystem (5)
And is used as an input to the auxiliary noise amplitude adjustment module (42) and to add up colored noise v generated by the auxiliary noise amplitude adjustment module (42) 0 And (n) the input is used as a reference input of the on-line identification subsystem (2) of the acoustic feedback channel, so that the performance of the on-line identification subsystem (2) of the acoustic feedback channel is integrally improved, and the influence of acoustic feedback is compensated.
7. Active noise control system according to claim 1, characterized in that the secondary channel in-line identification subsystem (4) separates the narrow-band components of the residual noise separation subsystem (5)
The sum is used as an input to the auxiliary noise amplitude adjustment module (42) and the colored noise v generated by the auxiliary noise amplitude adjustment module (42) 0 And (n) synchronously serving as a reference input of the acoustic feedback channel online identification subsystem (2) and a reference input of the secondary channel online identification module (41) respectively, and effectively reducing the contribution of auxiliary noise to residual noise while respectively ensuring the performances of the acoustic feedback channel online identification subsystem (2) and the secondary channel online identification subsystem (4), so that the overall noise reduction performance of the system tends to an ideal level.
8. A narrow-band feedforward type active noise control method employing the narrow-band feedforward type active noise control system according to claim 1 to 7, the method comprising:
step one: the secondary sound source synthesis subsystem (3) synthesizes the secondary sound source narrowband component and simultaneously generates auxiliary noise v with the auxiliary noise amplitude adjustment module (42) 0 (n) adding to synthesize a secondary sound source y (n);
step two: the secondary sound source y (n) generates an actual sound feedback signal y through an actual sound feedback channel f (n); actual reference signal x s (n) subtracting the actual acoustic feedback signal y f (n) obtaining a reference signal x r (n); reference signal x r (n) subtracting the acoustic feedback signal generated by the acoustic feedback channel estimation model (21)
As input to a reference signal synthesis subsystem (1); the reference signal synthesis subsystem (1) synthesizes a narrowband reference component and a wideband reference component respectively; at the same time, the secondary sound source y (n) generates anti-noise y through the actual secondary channel p (n); target noise p (n) minus anti-noise y p (n) obtaining residual noise e (n); at the same time, auxiliary noise v generated by the auxiliary noise amplitude adjustment module (42) 0 (n) serving as a reference input to the secondary channel on-line recognition module (41);
step three: residual noise e (n) passes through the residualThe residual error separation subsystem (5) separates into broadband separation d s (n) and narrowband component u i (n)；
Step four: a coefficient update iteration related to the center frequency of the IIR trap in the reference signal synthesis subsystem (1); meanwhile, updating and iterating coefficients of an acoustic feedback channel estimation model (21) in the acoustic feedback channel online identification subsystem (2) by using an LMS algorithm; meanwhile, the coefficients of a secondary channel estimation model (41) in the secondary channel online identification subsystem (4) are updated and iterated by using an LMS algorithm; meanwhile, the coefficients of the narrowband controller (31) in the secondary sound source synthesis subsystem (3) are updated and iterated by using a filtering-X least mean square algorithm module (32) with first-order delay;
step five: returning to the first step, repeating the first step to the fourth step until the system converges and reaches a steady state.
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