CN202838949U - Self-adaptive noise cancellation device - Google Patents

Abstract

The utility model discloses a self-adaptive noise cancellation device. The device comprises a first microphone, a second microphone, a first filter, a second filter and a subtracter. The first microphone inputs a received signal into the first filter. The first filter inputs the filtered signal into the subtracter. The second microphone inputs a received signal into the second filter. The second filter inputs the filtered signal into the subtracter. The subtracter carries out subtraction on the signals filtered by the first filter and the second filter to acquire a de-noised signal. In a noise segment, the coefficients of the first filter and the second filter are respectively updated according to the de-noised signal, thus the noise component in the signal filtered by the first filter and the noise component in the signal filtered by the second filter tend to be the same. According to the utility model, the problem of poor noise cancellation effect, which is caused by that an FIR filter is used and the optimal solution of noise cancellation cannot be approximated in the prior art, is solved.

Description

A kind of self-adaptation is eliminated the noise device

Technical field

The utility model relates to the signal process field, and particularly a kind of self-adaptation is eliminated the noise device.

Background technology

The LMS(Least Mean Square of prior art, lowest mean square) algorithm employing scalar filter structure, as shown in Figure 1.Its principle as shown in Figure 2, the signal that No. one microphone is wherein received carries out filtering, the signal that filtered signal and another road microphone receive subtracts each other the voice that obtain behind the noise reduction.The wave filter of this scalar filter structure only upgrades in noise segment, and in the noisy speech section, wave filter remains unchanged.

The time domain LMS algorithm of standard, non additivity interference noise for convolution, its computation complexity is larger, in order to reduce computation complexity, Ferrara has proposed FBLMS(Fast Block LMS, Fast Block lowest mean square) algorithm, what this algorithm adopted is the mode of time-frequency domain combination, the convolution algorithm that is about to originally carry out in time domain is transformed into the product calculation of frequency domain, thereby has greatly reduced the complexity of calculating.

Below defective that scalar filter structure LMS algorithm of the prior art is existed describe.

Set forth the defective that the scalar filter structure exists by the theoretical optimum solution of analysis list filter construction median filter.Owing to can clearly analyze the optimum solution of wave filter at frequency domain, therefore the analytical calculation of filter theory optimum solution being carried out at frequency domain.

As shown in Figure 3, be the analysis schematic diagram of scalar filter structure median filter frequency domain optimum solution.S1 representation signal source among Fig. 3, S2 represents noise source.Because FIR(Finite Impulse Response, finite impulse response (FIR)) wave filter can characterize information source comparatively accurately to the transport function of microphone, therefore in analysis, adopt the channel transfer functions H11 between FIR wave filter difference simulation signal generator and the first microphone, channel transfer functions H12 between noise source and the first microphone, channel transfer functions H21 between signal source and second microphone, the channel transfer functions H22 between noise source and second microphone.The signal that the first microphone receives is X1, and the signal that second microphone receives is X2, and W is wave filter, and Y1 is the signal behind the noise reduction.

Can obtain following formula:

X1=S1 * H11+S2 * H12 formula 1

X2=S1 * H21+S2 * H22 formula 2

Y1＝X1-X2×W＝(S1×H11+S2×H12)-(S1×H21+S2×H22)×W

Formula 3

＝S1×(H11-H21×W)+S2×(H12-H22×W)

Because when W gets optimum solution, noise source S2 will be completely eliminated, thereby the optimum solution that can release W as shown in Equation 4.

$H12-H22\×W=0\⇒W=H12/H22$ Formula 4

Y1=S1 * (H11-H21 * the W)=S1 * (formula 5 of H11-H21 * H12/H22)

By formula 5 as can be known Y1 be S1 through certain filtered form, do not contain any component of S2.

Can learn from the form of W=H12/H22 optimum solution obtained above, the optimum solution of W is not the FIR wave filter, but in actual applications in order to guarantee the stable and easy implementation of wave filter, usually adopt the FIR wave filter, this will introduce larger error, and its reason is to use a FIR wave filter can not well remove to approach a non-FIR wave filter.

The optimum solution right and wrong FIR wave filter of standard scalar filter structure LMS algorithm median filter, and the wave filter in this structure adopts the FIR wave filter to go to approach this optimum solution in actual applications usually, thereby can introduce larger error, cause the noise eradicating efficacy relatively poor.

The utility model content

The utility model provides a kind of self-adaptation to eliminate the noise device, causes eliminating the relatively poor problem of noise effects to solve prior art owing to using a FIR wave filter can't approach the optimum solution of eliminating noise.

The invention also discloses a kind of self-adaptation and eliminate the noise device, described device comprises: the first microphone, second microphone, the first wave filter, the second wave filter and subtracter,

The first microphone will receive signal and input the first wave filter, the first wave filter with filtering after signal input subtracter;

Second microphone will receive signal and input the second wave filter, the second wave filter with filtering after signal input subtracter;

The signal subtraction of subtracter after with the first wave filter and the second filter filtering draws signal behind the noise reduction;

Wherein, in noise segment, the coefficient of the first wave filter and the second filter coefficient upgrade according to signal behind the noise reduction respectively, so that the noise component that comprises in the signal behind the noise component that comprises in the signal behind the first filter filtering and the second filter filtering is tending towards identical; And,

In the noisy speech section, the coefficient of the coefficient of the first wave filter and the second wave filter remains unchanged respectively, the coefficient that the signal that the first wave filter receives the first microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded, and the coefficient that the signal that the second wave filter receives second microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded.

Wherein, the ratio of the transport function of the transport function of the first wave filter and the second wave filter approaches channel transfer functions between noise source and second microphone and the ratio of the channel transfer functions between noise source and the first microphone.

Wherein, the transport function of the first wave filter is approached the channel transfer functions between noise source and second microphone, and the transport function of the second wave filter is approached the channel transfer functions between noise source and the first microphone.

Wherein, the transport function of the first wave filter is approached channel transfer functions between noise source and second microphone and the product of constant, and the transport function of the second wave filter is approached channel transfer functions between noise source and the first microphone and the product of described constant.

Wherein, the coefficient of the first wave filter specifically by least mean square algorithm or Fast Block least mean square algorithm, upgrades according to signal behind the noise reduction;

The coefficient of the second wave filter specifically by least mean square algorithm or Fast Block least mean square algorithm, upgrades according to signal behind the noise reduction.

The beneficial effects of the utility model are: in noise segment, the coefficient of the first wave filter and the second filter coefficient upgrade according to signal behind the noise reduction respectively, so that the noise component that comprises in the signal behind the noise component that comprises in the signal behind the first filter filtering and the second filter filtering is tending towards identical; In the noisy speech section, the coefficient of the coefficient of the first wave filter and the second wave filter remains unchanged respectively, the coefficient that the signal that the first wave filter receives the first microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded, and the coefficient that the signal that the second wave filter receives second microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded; And then with the signal subtraction behind two filter filterings the time, the noise component in the signal is cancelled out each other substantially, be enhanced thereby eliminate noise effects.

Description of drawings

Fig. 1 is that the LMS of prior art adopts scalar filter to eliminate the schematic diagram of noise device.

Fig. 2 is that the LMS of prior art adopts scalar filter to eliminate the schematic diagram of noise device.

Fig. 3 is that the LMS of prior art adopts scalar filter to eliminate the principle analysis schematic diagram of the frequency domain optimum solution of noise.

Fig. 4 is the structural drawing that the self-adaptation of the utility model embodiment is eliminated the noise device.

Fig. 5 is the schematic diagram that the self-adaptation of the utility model embodiment is eliminated the noise device.

Fig. 6 is the principle analysis schematic diagram that the self-adaptation of the utility model embodiment is eliminated the noise device.

Fig. 7 is that the self-adaptation of the utility model embodiment is eliminated the schematic diagram that noise device time domain is processed.

Embodiment

For making the purpose of this utility model, technical scheme and advantage clearer, below in conjunction with accompanying drawing the utility model embodiment is described in further detail.

Embodiment one

Referring to Fig. 4, eliminate the structural drawing of noise device for the self-adaptation of the utility model embodiment.

Described device comprises: the first microphone 110, second microphone 120, the first wave filter 210, the second wave filter 220 and subtracter 300.

The first microphone 110 will receive signal and input the first wave filter 210, the first wave filters 210 with signal input subtracter 300 after the filtering;

Second microphone 120 will receive signal and input the second wave filter 220, the second wave filters 220 with signal input subtracter 300 after the filtering;

Subtracter 300 draws signal behind the noise reduction with the first wave filter 210 and the second wave filter 220 filtered signal subtractions.

Wherein, in noise segment, the coefficient of the coefficient of the first wave filter 210 and the second wave filter 220 upgrades according to signal behind the noise reduction respectively, so that the noise component that comprises in the noise component that comprises in the first wave filter 210 filtered signals and the second wave filter 220 filtered signals is tending towards identical;

And, in the noisy speech section, the coefficient of the coefficient of the first wave filter 210 and the second wave filter 220 remains unchanged respectively, the coefficient that the signal that 210 pairs of the first microphones 110 of the first wave filter receive uses when carrying out filtering is the coefficient after last time, noise segment was upgraded, and the coefficient that the signal that 220 pairs of second microphones of the second wave filter 120 receive uses when carrying out filtering is the coefficient after last time, noise segment was upgraded.

In one embodiment, the ratio of the transport function of the transport function of the first wave filter 210 and the second wave filter 220 approaches the ratio of the channel transfer functions of 110 of the channel transfer functions of 120 of noise source and second microphones and noise source and the first microphones.

The below is in the present embodiment, and the principle that self-adaptation is eliminated the device of noise describes.Fig. 5 is the schematic diagram that the self-adaptation of the utility model embodiment is eliminated the noise device.Fig. 6 is the principle analysis schematic diagram that the self-adaptation of the utility model embodiment is eliminated the noise device.

With reference to Fig. 6, S1 representation signal source, S2 represents noise source, it is the frequency domain value of the signal that receives on the first microphone 110 by X1, the frequency domain value of the signal that receives on the X2 second microphone 120, W1, W2 are respectively the transport function of the first wave filter 210 and the second wave filter 220, and Y1 is the frequency domain value of the signal behind the noise reduction.

Can obtain following formula.

X1=S1 * H11+S2 * H12 formula 6

X2=S1 * H21+S2 * H22 formula 7

Y1＝X1×W1-X2×W2＝(S1×H11+S2×H12)×W1-(S1×H21+S2×H22)×W2

=S1 * (H11 * W1-H21 * the W2)+S2 * (formula 8 of H12 * W1-H22 * W2)

Because when W gets optimum solution, noise source S2 will be completely eliminated, so exist between two wave filter W1 shown in the formula 9 and W2 and concern.

$\frac{W1}{\mathrm{<figure-callout\; id="2"\; label="W"\; filenames="HDA00002112100900032.png"\; state="\{\{state\}\}">W</figure-callout>}2}=\frac{H22}{H12}$ Formula 9

When the transport function relation of two wave filters satisfied formula 9, the signal behind the noise reduction was

$\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA00002112100900011.png,HDA00002112100900041.png"\; state="\{\{state\}\}">Y</figure-callout>}1=\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA00002112100900011.png,HDA00002112100900041.png"\; state="\{\{state\}\}">S</figure-callout>}1\&times;(H11\&times;W1-H21\&times;W2)=\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA00002112100900011.png,HDA00002112100900041.png"\; state="\{\{state\}\}">S</figure-callout>}1\&times;(H11\&times;H22-H21\&times;H12)\&times;\frac{W1}{H22}$ Formula 10

Y1 is certain filtered form of S1 process, and Y1 does not contain any component of S2 as the above analysis.

In the present embodiment, can be in several ways so that the transport function ratio of the first wave filter 210 and the second wave filter 220 approaches the ratio of the channel transfer functions of 110 of noise source and second microphone 120 and noise source and the first microphones.

For example, the transport function of the first wave filter 210 is approached the channel transfer functions of 120 of noise source and second microphones, and the transport function of the second wave filter 220 is approached the channel transfer functions of 110 of noise source and the first microphones.

As shown in Figure 6, eliminate the principle analysis schematic diagram of the device of noise for this for example middle self-adaptation.

The transport function of the first wave filter 210 is W1, W1=H22, and the transport function of the second wave filter 220 is W2, W2=H12.At this moment, the noise component in the signal is identical behind two filter filterings.Therefore, this for example in, make W1 approach H22, make W2 approach H12, can guarantee that the noise component that comprises in the first wave filter 210 and the second wave filter 220 filtered signals is identical as far as possible, thereby effectively eliminate noise.

Again for example, the transport function of the first wave filter 210 is approached the channel transfer functions of 120 of noise source and second microphones and the product of constant, and the transport function of the second wave filter 220 is approached the channel transfer functions of 110 of noise source and the first microphones and the product of described constant.This constant can be constant or certain transport function.Be W1=H22H, W2=H12H, H are a certain transport function or constant.

This for example in, guarantee that equally the noise component that comprises in the first wave filter 210 and the second wave filter 220 filtered signals is identical as far as possible, thereby effectively eliminate noise.

Owing to when the transport function relation of two wave filters satisfies formula 9, noise in the signal can be eliminated, therefore use two FIR wave filters to make its mutual relationship approach formula 9, the error of introducing so just obviously reduces, thereby has greatly promoted noise reduction.

In this mode, each filter coefficient of noise segment latest update last time that uses carries out filtering, and noise component is tending towards identical in the signal behind two filter filterings, and both cancel out each other, so that behind the noise reduction in the signal noise component constantly reduce, output voice quality constantly be enhanced.

Wherein, use least mean square algorithm or Fast Block least mean square algorithm to upgrade wave filter (the first wave filter 210 or the second wave filter 220) coefficient, so that transport function corresponding to filter approximating.

Fig. 7 is that the self-adaptation of the utility model embodiment is eliminated the schematic diagram that noise device time domain is processed, and eliminates the device of noise for adopting double filter.

The below specifies the coefficient update of the first wave filter 210 and the second wave filter 220 according to schematic diagram shown in Figure 7.

Adopt time domain LMS algorithm to upgrade double filter structure median filter coefficient.Signal is y (n) after 210 filtering of the first wave filter, and as shown in Equation 11, it is the signals with noise of input signal behind the first wave filter 210.Signal is d (n) after 220 filtering of the second wave filter, and as shown in Equation 12, it is the signals with noise of input signal behind the second wave filter 220.It is e (n) that two filter signals subtract each other rear output signal, as shown in Equation 13.

$y\left(n\right)=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{w}_{1i}\left(n\right)x_1(n-i)$ Formula 11

$d\left(n\right)=\underset{j=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{W}_{2j}\left(n\right)x_2(n-j)$ Formula 12

E (n)=d (n)-y (n) formula 13

Adopt the LMS algorithm that the transport function of wave filter is upgraded, the transport function of the first wave filter 210 by formula 14 is upgraded, and the transport function of the second wave filter 220 by formula 15 is upgraded.

$W_1(n+1)=W_1\left(n\right)-\mathrm{\&mu;}{\left[\begin{array}{cccc}\frac{{\&PartialD;e}^2\left(n\right)}{{\&PartialD;w}_{10}}& \frac{{\&PartialD;e}^2\left(n\right)}{{\&PartialD;w}_{11}}& \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;& \frac{{\&PartialD;e}^2\left(n\right)}{{\&PartialD;w}_{1(N-1)}}\end{array}\right]}^T=W_1\left(n\right)+2\mathrm{\&mu;e}\left(n\right)X_1\left(n\right)$ Formula 14

$W_2(n+1)=W_2\left(n\right)-\mathrm{\&mu;}{\left[\begin{array}{cccc}\frac{\&PartialD;e^2\left(n\right)}{\&PartialD;w_{20}}& \frac{\&PartialD;e^2\left(n\right)}{\&PartialD;w_{21}}& \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;& \frac{\&PartialD;e^2\left(n\right)}{\&PartialD;w_{2(N-1)}}\end{array}\right]}^T=W_2\left(n\right)-2\mathrm{\&mu;e}\left(n\right)X_2\left(n\right)$ Formula 15

W wherein ₁(n), W ₂(n), X ₁(n), X ₂(n) all represent column vector, subscript T represents transposition, and

X ₁(n)＝[x ₁(n)x ₁(n-1)……x ₁(n-N+1)] ^T

X ₂(n)＝[x ₂(n)x ₂(n-1)……x ₂(n-N+1)] ^T

The signal behind e (n) the expression noise reduction wherein, signal after 210 filtering of d (n) expression the first wave filter, signal after 220 filtering of y (n) expression the second wave filter, W ₁(n) transport function of expression the first wave filter 210, W ₂(n) transport function of expression the second wave filter 220, μ represents step factor, X ₁(n) represent the signal vector that the first microphone 110 receives, X ₂(n) represent the signal vector that second microphone 120 receives, N represents the exponent number of wave filter.

With reference to schematic diagram shown in Figure 5, specify the coefficient that the FBLMS algorithm that adopts the double filter structure upgrades the first wave filter 210 and the second wave filter 220.

The below provides the filter update formula of the FBLMS algorithm that adopts the double filter structure, wherein " * " expression convolution.

Wherein, signal is y (n) after 210 filtering of the first wave filter, and as shown in Equation 16, it is the signals with noise of input signal behind the first wave filter 210.Signal is d (n) after 220 filtering of the second wave filter, and as shown in Equation 17, it is the signals with noise of input signal behind the second wave filter 220.It is e (n) that two filter signals subtract each other rear output signal, as shown in Equation 18.

Y (n)=w ₁(n) * x ₁(n) formula 16

D (n)=w ₂(n) * x ₂(n) formula 17

E (n)=d (n)-y (n) formula 18

Formula 18 is done FFT(Fast Fourier Transform, Fast Fourier Transform (FFT)) transform to frequency domain as shown in Equation 19

E (k)=D (k)-Y (k)=W ₂(k) X ₂(k)-W ₁(k) X ₁(k) formula 19

Adopt the following formula of the principle of FBLMS algorithm.

${\&dtri;W}_1\left(k\right)\&Proportional;\frac{\&PartialD;{\left[E\left(k\right)\right]}^2}{\&PartialD;W_1\left(k\right)}=2\&CenterDot;E\left(k\right)\&CenterDot;\frac{\&PartialD;\left[E\left(k\right)\right]}{\&PartialD;W_1\left(k\right)}=-2E\left(k\right)\&CenterDot;\stackrel{\&OverBar;}{X_1\left(k\right)}$ Formula 20

$\&dtri;W_2\left(k\right)\&Proportional;\frac{\&PartialD;{\left[E\left(k\right)\right]}^2}{\&PartialD;W_2\left(k\right)}=2\&CenterDot;E\left(k\right)\&CenterDot;\frac{\&PartialD;\left[E\left(k\right)\right]}{\&PartialD;W_2\left(k\right)}=2E\left(k\right)\&CenterDot;\stackrel{\&OverBar;}{X_2\left(k\right)}$ Formula 21

$W_1(k+1)=W_1\left(k\right)-\mathrm{\&mu;}\&CenterDot;\&dtri;W_1\left(k\right)=W_1\left(k\right)+2\&CenterDot;\mathrm{\&mu;}\&CenterDot;E\left(k\right)\&CenterDot;\stackrel{\&OverBar;}{X_1\left(k\right)}$ Formula 22

$W_2(k+1)=W_2\left(k\right)-\mathrm{\&mu;}\&CenterDot;\&dtri;W_2\left(k\right)=W_2\left(k\right)-2\&CenterDot;\mathrm{\&mu;}\&CenterDot;E\left(k\right)\&CenterDot;\stackrel{\&OverBar;}{X_2\left(k\right)}$ Formula 23

The signal behind e (n) the expression noise reduction wherein, E (k) is the frequency domain representation of e (n), signal after 210 filtering of d (n) expression the first wave filter, D (k) is the frequency domain representation of d (n), signal after 220 filtering of y (n) expression the second wave filter, Y (k) is the frequency domain representation of y (n), X ₁(k) be the frequency domain representation of the signal that receives of the first microphone 110, X ₂(k) be the frequency domain representation of the signal of second microphone 120 receptions, W ₁, W ₂The frequency domain representation of the transport function of expression sef-adapting filter, μ represents step factor,

Expression X ₁(k) conjugation,

Expression X ₂(k) conjugation.

Based on formula 22 and formula 23, adopt the FBLMS algorithm to carry out filter coefficient update.

1, filtering

If length is that two frequency domain filters of N are w _F1(k), w _F2(k), all fill N zero before and after the signal that the first microphone 110 and second microphone 120 are received, then carry out piecemeal and obtain the block signal that length is L+N-1

The overlapping N of an interblock data.

$x_{F1}\left(k\right)=\mathrm{FFT}\left({\tilde{x}}_1\left(k\right)\right)$ Formula 24

$x_{F2}\left(k\right)=\mathrm{FFT}\left({\tilde{x}}_2\left(k\right)\right)$ Formula 25

$y\left(k\right)=\mathrm{IFFT}(x_{F1}\left(k\right)\&CircleTimes;W_{F1}\left(k\right))$ Formula 26

$d\left(k\right)=\mathrm{IFFT}(x_{F2}\left(k\right)\&CircleTimes;W_{F2}\left(k\right))$ Formula 27

Wherein k=1:L+N-1 represents 1 to L+N-1,

The expression dot product, IFFT represents inverse fast Fourier transform (Inverse Fast Fourier Transform), the signal indication frequency-region signal of subscript " F ".

2, estimation of error

E (m)=d (N:L+N-1)-y (N:L+N-1) formula 28

Wherein, m=1:L(represents 1 to L); D (N:L+N-1) is last L the element of d in the formula 27 (k), and is corresponding with d (n) among Fig. 5; Y (N:L+N-1) is last L the element of y in the formula 26 (k), and is corresponding with y (n) among Fig. 5.E (m) is the signal behind the noise reduction.

3, filter update

Formula 29

$W_{F1}(k+1)=W_{F1}\left(k\right)+2\mathrm{\&mu;}\&CircleTimes;\stackrel{\&OverBar;}{x_{F1}\left(k\right)}\&CircleTimes;e_F\left(k\right)$ Formula 30

$W_{F2}(k+1)=W_{F2}\left(k\right)-2\mathrm{\&mu;}\&CircleTimes;\stackrel{\&OverBar;}{x_{F2}\left(k\right)}\&CircleTimes;e_F\left(k\right)$ Formula 31

4, wave filter constraint

Formula 32

Formula 33

Comprise unnecessary misdata in the filter transfer function in formula 30 and the formula 31, by formula 32 and formula 33, fill zero after the unnecessary misdata of in transport function, skimming.

The above is preferred embodiment of the present utility model only, is not be used to limiting protection domain of the present utility model.All any modifications of within spirit of the present utility model and principle, doing, be equal to replacement, improvement etc., all be included in the protection domain of the present utility model.

Claims (5)

1. a self-adaptation is eliminated the noise device, it is characterized in that, described device comprises: the first microphone, second microphone, the first wave filter, the second wave filter and subtracter,

The first microphone will receive signal and input the first wave filter, the first wave filter with filtering after signal input subtracter;

Second microphone will receive signal and input the second wave filter, the second wave filter with filtering after signal input subtracter;

The signal subtraction of subtracter after with the first wave filter and the second filter filtering draws signal behind the noise reduction;

Wherein, in noise segment, the coefficient of the first wave filter and the second filter coefficient upgrade according to signal behind the noise reduction respectively, so that the noise component that comprises in the signal behind the noise component that comprises in the signal behind the first filter filtering and the second filter filtering is tending towards identical; And,

In the noisy speech section, the coefficient of the coefficient of the first wave filter and the second wave filter remains unchanged respectively, the coefficient that the signal that the first wave filter receives the first microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded, and the coefficient that the signal that the second wave filter receives second microphone uses when carrying out filtering is the coefficient after last time, noise segment was upgraded.

2. device according to claim 1 is characterized in that,

The ratio of the transport function of the transport function of the first wave filter and the second wave filter approaches channel transfer functions between noise source and second microphone and the ratio of the channel transfer functions between noise source and the first microphone.

3. device according to claim 2 is characterized in that,

The transport function of the first wave filter is approached the channel transfer functions between noise source and second microphone, and the transport function of the second wave filter is approached the channel transfer functions between noise source and the first microphone.

4. device according to claim 2 is characterized in that,

The transport function of the first wave filter is approached channel transfer functions between noise source and second microphone and the product of constant, and the transport function of the second wave filter is approached channel transfer functions between noise source and the first microphone and the product of described constant.

5. device according to claim 1 is characterized in that,

The coefficient of the first wave filter specifically by least mean square algorithm or Fast Block least mean square algorithm, upgrades according to signal behind the noise reduction;

The coefficient of the second wave filter specifically by least mean square algorithm or Fast Block least mean square algorithm, upgrades according to signal behind the noise reduction.

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