CN110599997B - Impact noise active control method with strong robustness - Google Patents

Impact noise active control method with strong robustness Download PDF

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CN110599997B
CN110599997B CN201910909005.9A CN201910909005A CN110599997B CN 110599997 B CN110599997 B CN 110599997B CN 201910909005 A CN201910909005 A CN 201910909005A CN 110599997 B CN110599997 B CN 110599997B
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赵海全
朱迎莹
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Southwest Jiaotong University
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
    • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
    • G10K11/17823—Reference signals, e.g. ambient acoustic environment
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
    • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
    • G10K11/17825—Error signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1787—General system configurations
    • G10K11/17879—General system configurations using both a reference signal and an error signal
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters

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Abstract

An impact noise active control method with strong robustness mainly comprises the following steps: A. acquiring a reference signal, wherein the discrete value x (n) of the noise signal of the current moment n is acquired by a reference microphone, and the input signal vector of a filter is X (n); B. generating a filter coefficient, and filtering the noise input vector X (n) by a filter according to the weight coefficient vector W (n) to obtain an output value y (n) of the filter; C. generating a noise-canceling signal, and obtaining a noise-canceling signal y' (n) after the output value y (n) of the filter passes through a secondary path S (z); D. calculating the average value of p-order moments of the residual signals; E. exponential residual signal p-order moment average cpAnd (n), F, updating the calculation of the gradient value delta (n), G, and updating to obtain W (n +1) as the weight coefficient vector of the next time n +1, wherein W (n +1) is W (n) + mu delta (n) x (n). The method has strong robustness to impact noise, small steady-state error and good noise reduction performance.

Description

Impact noise active control method with strong robustness
Technical Field
The invention relates to an active noise control method, in particular to an active control method for impact noise.
Background
With the rapid development of modern industry, noises in different forms, such as automobile engine noise, noise in train operation, transformer noise and the like, gradually become a non-negligible interference factor in daily life and production of people. The excessive noise can affect the normal life of people, reduce the labor productivity, harm the health of human bodies and even directly cause certain diseases. Therefore, how to effectively reduce the environmental noise becomes a problem to be solved urgently.
The traditional noise reduction technology is Passive Noise Control (PNC), and most of the technologies use acoustic materials to reflect and absorb sound waves, but the method is widely used, but the method only has a good effect on high-frequency noise, but has a small effect on low-frequency noise. For low-frequency noise such as transformer noise and motor noise, a novel noise control method is required.
The Active Noise Control (ANC) is based on the principle that a reference microphone collects source noise at a noise source, an analog-to-digital converter converts input noise into a digital signal, and a circuit and a loudspeaker emit secondary sound waves with the same amplitude frequency and opposite phases of the noise after the operation of an adaptive control algorithm. The waveform of the secondary sound wave and the source noise are cancelled, and the control of the noise is realized.
The least mean square (FxLMS) filtering algorithm for minimizing the residual error becomes a main algorithm for active noise control by the characteristics of low calculation cost, compact structure and the like, and is widely applied to the common Gaussian noise environment. But for the impact noise with large amplitude and violent change, the sensitivity of the minimum residual mean square (second moment of the residual) (FxLMS) filtering algorithm to the impact noise is high, the stability of the algorithm is low, and the noise control effect is poor; the p-norm filtering algorithm for minimizing residual has a significant improvement on the control effect of impulsive noise, as disclosed in document 1, "Leahy R, Zhou Z, Hsu YC.adaptive filtering of stable processes for active actuation of impulse noise. in: Proceedings of the 1995International Conference on Acoustics, Speech, and Signal Processing, vol.5; 1995, pp. 2983-; however, for strong impact noise, the suppression effect of the algorithm is limited, and the situations of iteration unconvergence and instability are prone to occur, and the robustness of the algorithm needs to be improved.
Disclosure of Invention
The invention aims to provide an impact noise active control method with strong robustness, which has small steady-state error of impact noise, strong robustness and good noise reduction effect.
The invention adopts the technical scheme that an impact noise active control method with strong robustness comprises the following steps:
A. noise signal acquisition
A reference microphone near a noise source collects a noise signal discrete value x (n) of the current time n, and the noise signal discrete values x (n), x (n-1), x (n-L +1) from the current time n to the previous L-1 times form a noise signal vector X (n) of the current time n，X(n)＝[x(n),x(n-1),...,x(n-L+1)]T(ii) a Where L is 128, which is the number of taps of the filter, and the superscript T represents the transpose operation;
B. filter coefficient generation
The filter generates weight coefficients w (n), w (n-1), …, w (n-L +1) of the current time n and the previous L-1 times, and the L weight coefficients form a weight coefficient vector W (n) of the current time n, W (n) ═ w (n), w (n-1),. once, w (n-L +1) ]; when current time n < 129, w (n) is 0;
C. noise cancelling signal generation
Inputting the noise signal vector X (n) of the current time n into the filter to obtain the output value y (n) of the current time n of the filter, wherein y (n) is WT(n)X(n)；
The output value y (n) of the filter passes through a secondary path S (z) consisting of a D/A, a reconstruction filter, a power amplifier, a noise elimination loudspeaker and an error microphone of a noise elimination point, and a noise elimination signal y '(n) is obtained at the noise elimination point, wherein y' (n) is s (n) y (n); wherein symbol denotes the convolution operation, s (n) denotes the impulse response of the secondary path s (z);
D. calculation of the mean value of the p-th moments of the residual signal
The error microphone collects a noise-eliminating signal y' (n) of a noise-eliminating point at the current time n and a sound signal acted by a noise signal discrete value x (n) of the current time n, and the sound signal is used as a residual signal e (n) of the current time n and is sent to a filter; the filter calculates the p-order moment of the residual signal at the current time n at p e [1,2 ] according to the residual signal e (n) at the current time n]Mean value c within the intervalp(n)，
Figure BDA0002214160220000031
In which | e (n) is non-conductingpRepresents the p-order moment of the residual signal e (n), dp represents the differential to the order moment p, | e (n) | represents the absolute value of the residual signal e (n);
E. exponential averaging of p-order moment of residual signal
The filter calculates an exponential average value g (n) of the p-th moment of the residual signal at the current time n, g (n) exp { - η cp(n), wherein exp (·) represents exponential operation, η is exponential parameter, whose value is smallA positive number at 10;
F. calculation of gradient vectors
The filter calculates the updating gradient value delta (n) of the weight value n at the current moment,
Figure BDA0002214160220000032
wherein sign (·) represents a sign operation;
G. updating of weight coefficient vectors
The filter updates the gradient value delta (n) according to the weight value n at the current moment, and the weight coefficient vector W (n +1) of the next moment n +1 is obtained through updating, wherein W (n +1) W (n) + mu delta (n) X (n); in the formula, mu is a step factor, and the value range of mu is 0.01-0.1;
H. iteration
And (4) making n equal to n +1, and repeating the steps A to G until the noise control is finished.
Compared with the prior art, the invention has the beneficial effects that:
firstly, under a strong impact noise environment, residual signals can fluctuate, and noise control has high requirements on the stability of a control method; comparing p-order moment | e (n) determined by a p-value of residual signal in reference 1pAs the basis for estimating noise impact sound, the invention adopts the residual signal in p E [1,2 ∈]Average value c of p-order moments of all p values in intervalp(n)，
Figure BDA0002214160220000041
P-order moment | e (n) determined instead of one p-value of document 1p(ii) a The average value of p-order moments of all p values can adapt to most kinds of impact noise, and the adaptability is stronger; and the selection of the p value is not required to be carried out by prior knowledge, so that the active control of the impact noise is easier to realize.
Second, generalized maximum entropy exp (-eta | e (n) of residual signal e (n)p) The residual signal in the mapping feature space comprises a higher-order absolute moment of a second-order moment. The invention uses the mapping mechanism of generalized maximum entropy for reference, and belongs to [1,2 ] for residual signals at p]Average value C of p-order moments in intervalp(n) carrying out operation similar to generalized maximum entropy to obtain an average value c of p-order momentp(n) exponential valueg(n)，g(n)＝exp{-ηCp(n), the minimum of the indexed value g (n) is used as a criterion of noise estimation to obtain an updated increment value of a filter weight coefficient, and the indexed updated increment value can effectively track the change of a residual signal e (n) under an impact noise environment and effectively slow down the huge change of the residual signal e (n) brought by strong impact noise, thereby obviously reducing the situations of unconvergence and instability of iteration and having strong robustness; the steady-state error of the impact noise is small, and the noise reduction effect is good.
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Drawings
Fig. 1(a) shows the impact noise of α stable distribution with α ═ 1.6 used in the simulation experiment of the present invention.
Fig. 1(b) shows strong impact noise with a stable distribution, where α is 1.3, used in the simulation experiment of the present invention.
Fig. 1(c) shows the super-strong impact noise with a stable distribution, where α is 1.1, used in the simulation experiment of the present invention.
Fig. 2a is a graph of the average noise residual after the impact noise of fig. 1a is processed by the simulation experiment of document 1 and the method of the present invention.
Fig. 2b is a graph of the average noise residual after the primary noise of fig. 1b is processed by the simulation experiment of document 1 and the method of the present invention.
Fig. 2c is a graph of the average noise residual after the primary noise of fig. 1c is processed by the simulation experiment of document 1 and the method of the present invention.
Detailed Description
Examples
The invention relates to a method for actively controlling impact noise with strong robustness, which comprises the following steps:
A. noise signal acquisition
A reference microphone near a noise source collects a noise signal discrete value x (n) of the current time n, and noise signal discrete values x (n), x (n-1), are from the current time n to the previous L-1 times, x (n-1), are read, x (n-L +1) to form a noise signal vector X (n) of the current time n, wherein X (n) is [ x (n), x (n-1),]T(ii) a WhereinL-128, which is the number of taps of the filter, and the superscript T represents the transposition operation;
B. filter coefficient generation
The filter generates weight coefficients w (n), w (n-1), …, w (n-L +1) of the current time n and the previous L-1 times, and the L weight coefficients form a weight coefficient vector W (n) of the current time n, W (n) ═ w (n), w (n-1),. once, w (n-L +1) ]; when current time n < 129, w (n) is 0;
C. noise cancelling signal generation
Inputting the noise signal vector X (n) of the current time n into the filter to obtain the output value y (n) of the current time n of the filter, wherein y (n) is WT(n)X(n)；
The output value y (n) of the filter passes through a secondary path S (z) consisting of a D/A, a reconstruction filter, a power amplifier, a noise elimination loudspeaker and an error microphone of a noise elimination point, and a noise elimination signal y '(n) is obtained at the noise elimination point, wherein y' (n) is s (n) y (n); wherein symbol denotes the convolution operation, s (n) denotes the impulse response of the secondary path s (z);
D. calculation of the mean value of the p-th moments of the residual signal
The error microphone collects a noise-eliminating signal y' (n) of a noise-eliminating point at the current time n and a sound signal acted by a noise signal discrete value x (n) of the current time n, and the sound signal is used as a residual signal e (n) of the current time n and is sent to a filter; the filter calculates the p-order moment of the residual signal at the current time n at p e [1,2 ] according to the residual signal e (n) at the current time n]Mean value c within the intervalp(n)，
Figure BDA0002214160220000061
In which | e (n) is non-conductingpRepresents the p-order moment of the residual signal e (n), dp represents the differential to the order moment p, | e (n) | represents the absolute value of the residual signal e (n);
E. exponential averaging of p-order moment of residual signal
The filter calculates an exponential average value g (n) of the p-th moment of the residual signal at the current time n, g (n) exp { - η cp(n), wherein exp (·) represents exponential operation, η is an exponential parameter, and the value thereof is a positive number smaller than 10;
F. calculation of updated gradient values
The filter calculates the updating gradient value delta (n) of the weight value n at the current moment,
Figure BDA0002214160220000062
wherein sign (·) represents a sign operation;
G. updating of weight coefficient vectors
The filter updates the gradient value delta (n) according to the weight value n at the current moment, and the weight coefficient vector W (n +1) of the next moment n +1 is obtained through updating, wherein W (n +1) W (n) + mu delta (n) X (n); in the formula, mu is a step factor, and the value range of mu is 0.01-0.1;
H. iteration
And (4) making n equal to n +1, and repeating the steps A to G until the noise control is finished.
Simulation experiment:
to verify the effectiveness of the present invention, simulation experiments were performed and compared with the method of document 1.
A main-level path and a secondary-level path of a simulation experiment are both modeled by adopting a high-order FIR filter. The order L of the filter is set to 128.
The impact noise is modeled by a standard Alpha stable distribution noise model, and the characteristic function of Alpha stable distribution is as phi (t) e-|t|αAnd the smaller the value of alpha is, the stronger the impact noise is.
The impact noise of three α stable distributions (α ═ 1.6, 1.3, 1.1) were used in the experiment, as shown in fig. 1(a), 1(b), and 1 (c).
Average noise residual (ratio of residual signal after noise control processing to impulse noise without noise control processing at a noise elimination point) after noise control simulation of three impulse noises shown in fig. 1(a), 1(b) and 1(c) by the method of the present invention is shown in fig. 2a, 2b and 2 c. In fig. 2a, 2b, and 2c, the curves strung with a symbol "o" are the average noise residual curves of the method of document 1, and the curves strung with a symbol "it" are the average noise residual curves of the method of the present invention.
As can be seen from the simulation results of fig. 2a, 2b, and 2c, the method of the present invention has better control over the impact noise, and the average noise residual is much lower than that of the method of document 1; the stronger the impact noise is, the more obvious the advantages of the method are; the method of document 1 cannot even converge in fig. 2b and 2 c. Therefore, the method has the advantages of strong robustness, good stability, small steady-state error and good noise reduction effect on the impact noise.

Claims (1)

1. An impact noise active control method with strong robustness comprises the following steps:
A. noise signal acquisition
A reference microphone near a noise source collects a noise signal discrete value x (n) of the current time n, and noise signal discrete values x (n), x (n-1), are from the current time n to the previous L-1 times, x (n-1), are read, x (n-L +1) to form a noise signal vector X (n) of the current time n, wherein X (n) is [ x (n), x (n-1),]T(ii) a Where L is 128, which is the number of taps of the filter, and the superscript T represents the transpose operation;
B. filter coefficient generation
The filter generates weight coefficients w (n), w (n-1), …, w (n-L +1) of the current time n and the previous L-1 times, and the L weight coefficients form a weight coefficient vector W (n) of the current time n, W (n) ═ w (n), w (n-1),. once, w (n-L +1) ]; when current time n < 129, w (n) is 0;
C. noise cancelling signal generation
Inputting the noise signal vector X (n) of the current time n into the filter to obtain the output value y (n) of the current time n of the filter, wherein y (n) is WT(n)X(n)；
The output value y (n) of the filter passes through a secondary path S (z) consisting of a D/A, a reconstruction filter, a power amplifier, a noise elimination loudspeaker and an error microphone of a noise elimination point, and a noise elimination signal y '(n) is obtained at the noise elimination point, wherein y' (n) is s (n) y (n); wherein symbol denotes the convolution operation, s (n) denotes the impulse response of the secondary path s (z);
D. calculation of the mean value of the p-th moments of the residual signal
The error microphone collects a noise-eliminating signal y' (n) of a noise-eliminating point at the current time n and a sound signal acted by a noise signal discrete value x (n) of the current time n as a current timen residual signal e (n) and sending to a filter; the filter calculates the p-order moment of the residual signal at the current time n at p e [1,2 ] according to the residual signal e (n) at the current time n]Mean value c within the intervalp(n)，
Figure FDA0002214160210000021
In which | e (n) is non-conductingpRepresents the p-order moment of the residual signal e (n), dp represents the differential to the order moment p, | e (n) | represents the absolute value of the residual signal e (n);
E. exponential averaging of p-order moment of residual signal
The filter calculates an exponential average value g (n) of the p-th moment of the residual signal at the current time n, g (n) exp { - η cp(n), wherein exp (·) represents exponential operation, η is an exponential parameter, and the value thereof is a positive number smaller than 10;
F. calculation of gradient vectors
The filter calculates the updating gradient value delta (n) of the weight value n at the current moment,
Figure FDA0002214160210000022
wherein sign (·) represents a sign operation;
G. updating of weight coefficient vectors
The filter updates the gradient value delta (n) according to the weight value n at the current moment, and the weight coefficient vector W (n +1) of the next moment n +1 is obtained through updating, wherein W (n +1) W (n) + mu delta (n) X (n); in the formula, mu is a step factor, and the value range of mu is 0.01-0.1;
H. iteration
And (4) making n equal to n +1, and repeating the steps A to G until the noise control is finished.
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