CN103475980B - A kind of self adaptation acoustic feedback removing method - Google Patents

Abstract

The invention discloses a kind of self adaptation acoustic feedback removing method, aim to provide a kind of acoustic feedback removing method with higher gain and good robustness, its concrete steps are: one, main microphone picks up audio signal<i>D (n)</i>,<i>D (n)</i>Comprise far-end speech signal and near-end acoustic feedback signal composition, reference microphone is picked up acoustic feedback signal<i>X (n)</i>; Two, right<i>D (n)</i>With<i>X (n)</i>Adopt self adaptation elimination algorithm, be output as<i>E (n)</i>; Three, right<i>E (n)</i>Carry out digital-to-analogue conversion and power amplification; Four, by loudspeaker output audio signal. This scheme can accurately be eliminated environmental noise, prevent to clamor and cry, and gains high, and processing speed is fast, is applicable to the public address environment such as classroom, meeting-place.

Description

A kind of self adaptation acoustic feedback removing method

Technical field

The present invention relates to sound signal processing field, especially relate to a kind of at the sound adaptive for loudspeakerFeedback removing method.

Background technology

Public address occasion at microphone and loudspeaker composition can be simplified to shown in Fig. 1, and the sound that loudspeaker send is logicalCross acoustic feedback and again enter microphone, form a closed-loop system.

Pulsed transfer function between sound reinforcement system output and input closes and is:Wherein G₁(z) be the transfer function of microphone. If system meets following two on any one Frequency point simultaneouslyIndividual condition:

(1) amplitude conditions: | G₁(z)·G(z)·F(z)|≥1；

(2) phase condition: ∠ G₁(z) G (z) F (z)=2 π n, when n ∈ N;

Now, only need the triggering of small energy x (t), can produce vibration, occur uttering long and high-pitched sounds. Appearance prevents from uttering long and high-pitched soundsTechnology be called acoustic feedback inhibition technology.

The method that the most simply suppresses to utter long and high-pitched sounds is to select to have the microphone of closely saying characteristic, and source of sound is away from microphoneG when 50cm is above₁(z) amplitude is very little, now produces very little acoustic feedback gain. The defect of this method is to sayWords person's microphone of must moving close to could be realized public address, has placed restrictions on talker's scope of activities. Pick up if increaseTerritory, the range of sound, just must select high sensitivity microphone, the G of high sensitivity microphone₁(z) amplitude is larger, easilyProduce self-oscillation, produce and utter long and high-pitched sounds.

The main purpose that acoustic feedback suppresses technology is to destroy the condition of uttering long and high-pitched sounds and producing, and one is to destroy phase condition,Another kind is to destroy amplitude conditions.

By to feedback signal Phase Processing, prevent positive feedback and reach and eliminate the method for uttering long and high-pitched sounds and have shift frequency methodOr phase-shifting method. Owing to destroying, phase condition in all frequencies of 20HZ～20KHZ is very difficult, and its gain is carriedRising can only be within 3～5db, and its stability is subject to the impact of input channel.

Reach by suppressing the amplitude of acoustic feedback signal that to suppress one of common method of uttering long and high-pitched sounds be adaptive notchMethod. The method sets some static state and falls into filter frequency and some filter frequencies that dynamically falls into, and reduction may produce utters long and high-pitched soundsThe loop gain of frequency. In order to prevent the impact of wave trap method on tonequality, require trapper to there is narrow-band characteristic,The estimation of the frequency of uttering long and high-pitched sounds must be very accurate, and the frequency of accurately and accurately estimating to utter long and high-pitched sounds is very difficult, wave trap methodCompromise between accuracy and tonequality must estimated. Meanwhile, wave trap method also can be by mistake by defeated sinusoidal reality classEntering signal eliminates. Falling into the gain in actual applications of filter method is lifted between 5～8db.

Reaching by the amplitude of inhibition acoustic feedback signal the another kind of method that suppresses to utter long and high-pitched sounds is adaptive filtering method of offsetAlgorithm, the method is estimated acoustic feedback transfer function by self-adaptive processing, eliminates or reduce acoustic feedback signal to reachTo the object that suppresses to utter long and high-pitched sounds. The feature of this method is while estimating audio feedback path in the speech moment, feedback letterNumber with primary input signal there is high correlation, have a strong impact on the estimated accuracy of audio feedback path, and estimate receiveHold back speed, system background noise and noise type be also affect audio feedback path estimate important factor in order itOne. Some scholar proposes to reduce acoustic feedback feed back input and primary input by introduce appropriate time delay in processing linksCorrelation, some scholar actively dynamically makes an uproar adopting self adaptation acoustic feedback dynamically first to adopt before estimatingSound elimination algorithm reduces system background noise, and then improves the precision that follow-up acoustic feedback is estimated. How to find and closeThe suitable estimation moment, how to design efficient active noise reduction algorithm fast and be should be in this way prerequisite.

The index of evaluating acoustic feedback inhibition technology mainly contains two, and one is objective indicator: what public address gained carriesRise amount, adopt the public address gain not producing after acoustic feedback inhibition technology while uttering long and high-pitched sounds to compare and do not adopt before this technologyThe difference of public address gain; Another subjective index: the distortion factor of tonequality. Adopt after acoustic feedback inhibition technology,Unavoidably can process voice signal, process distortion thereby produce audio frequency. Shift frequency method and phase-shifting method are steadySurely spend bad, gain promote also little; It is larger that the sealing in of the multiple trappers of adaptive resistance-trap filtering caused tonequalityImpact, because trap frequency is difficult to accurately judge that gain lifting amplitude is also little. Adaptive-filtering method formerReason is to offset audio feedback path, if audio feedback path is estimated accurately not only not affect tonequality, also can eliminate skyBetween impact on direct sound wave. But adaptive filtering method of offset algorithm has been introduced closed loop feedback processing in algorithm,If it is inaccurate that audio feedback path is estimated, can produce on the contrary new uttering long and high-pitched sounds, namely the robustness of system is bad.

Summary of the invention

The present invention solves the existing gain of prior art to promote the technology that amplitude is little, robustness is badProblem, provides one to have higher gain and good robustness, can accurately eliminate environmental noise, prevent from producingThe raw self adaptation acoustic feedback removing method of clamoring and crying.

The present invention is directed to above-mentioned technical problem is mainly solved by following technical proposals: a kind of adaptiveAnswer acoustic feedback removing method, it is characterized in that, comprise the following steps:

One, main microphone picks up audio signal d (n), and d (n) comprises far-end speech signal and near-end acoustic feedback signalComposition, reference microphone is picked up acoustic feedback signal x (n);

Two, d (n) and x (n) are adopted to self adaptation elimination algorithm, are output as e (n), be specially:

WhenFrequency characteristic approachTime, can eliminateAcoustic feedback signal;

Adopt PNLMS algorithm to estimate that actual reference detects audio feedback path H (z), recursive algorithm is as follows:

x(n)＝[x(n)x(n-1)…x(n-N+1)]^T

$\hat{f}\left(n\right)={\hat{h}}^T\left(n\right)x\left(n\right)$

$e\left(n\right)=d\left(n\right)-\hat{f}\left(n\right)$

$h(n+1)=h\left(n\right)+\frac{\mathrm{\&mu;}G(n+1)}{\mathrm{\&alpha;}+x^T\left(n\right)G(n+1)x\left(n\right)}e\left(n\right)x\left(n\right)$

Wherein:

G(n+1)＝diag{g₁(n+1),g₂(n+1)…,g_N(n+1)}

$\begin{array}{c}{\mathrm{\&gamma;}}_{\min }(n+1)=\mathrm{\&rho;}\&CenterDot;\max \{{\mathrm{\&delta;}}_p,|{\hat{h}}_1\left(n\right)|,\\ \left|{\hat{h}}_2\left(n\right)\right|,\mathrm{...},\left|{\hat{h}}_N\left(n\right)\right|\}\end{array}$

${\mathrm{\&gamma;}}_k(n+1)=max\{{\mathrm{\&gamma;}}_{min}(n+1),|{\hat{h}}_k\left(n\right)\left|\right\}$

$g_k(n+1)=\frac{{\mathrm{\&gamma;}}_k(n+1)}{\frac{1}{N}\underset{i=1}{\overset{N}{\&Sigma;}}{\mathrm{\&gamma;}}_i(n+1)},1\&le;k\&le;N$

In above-mentioned formula, μ is step factor, and α is positive integer, and ρ is the parameter that affects global convergence speed;In the time that the maximum of input signal x (n) is normalized into 1, in above-mentioned formula, the value of step factor μ approximates0.01, convergence rate and μ are inversely proportional to; α is a little positive number, and object is to prevent that energy from becoming zero, generally getsValue 0.0001. ρ affects global convergence speed, and value is approximately equal to 5/N.Refer to echo path estimation FIRThe unit impulse response of wave filter; N refers to the order of echo path FIR wave filter; T is transposition symbol;Refer to echo estimation output;Refer to k estimation coefficient of echo path estimation FIR wave filter;

Three, e (n) is carried out to digital-to-analogue conversion and power amplification;

Four, by loudspeaker output audio signal.

As preferably, the e of step 2 (n) outputs to digital-to-analogue conversion after time delay module again.

As preferably, the difference that reference microphone and main microphone pick up the intensity of acoustic feedback audio signal needs littleIn 3db; The difference that reference microphone and main microphone pick up the intensity of voice audio signals need be greater than 20db.

As preferably, to process window slip interval and equal 6 sampled points, every 6 sampled points complete once adaptiveAnswer algorithm computing.

As preferably, the sample frequency of AD converter is 48khz, and resolution ratio is not less than 20bit.

As preferably, FIR filter order is not less than 128.

The present invention adopts both-end to detect input, with a reference microphone detection acoustic feedback audio signal, main wheatGram wind detects acoustic feedback and voice audio signals simultaneously.

Two detection signals are eliminated the acoustic feedback audio signal in reference microphone through sef-adapting filter, soAfter voice signal is amplified.

In order to reduce the correlation of acoustic feedback audio signal and voice audio signals in main microphone, according to voiceThe non-stationary feature of signal and do not affecting under the prerequisite of human auditory system susceptibility time delay after self-adaptive processing5ms～10ms exports left and right again.

The present invention adopts straight feedback Processing Algorithm, and algorithm stability is high, can be as universal adaptive audio feedback pathElimination algorithm produces self-oscillation like that.

On the other hand, the present invention adopts adaptive cancellation technology, in eliminating acoustic feedback, automatically realizesAdaptive active decrease of noise functions, disappear and both-end active adaptive noise reduction is current generally acknowledged best noiseEliminating method.

To achieve these goals, integrated feed forward type acoustic feedback inhibition method needs following steps with realization:

(1) the audio signal sample stage

A. the present invention must configure two microphones and detect audio signal;

B. one of them be muting sensitivity closely say microphone (reference microphone), obtain the near-end that loudspeaker sendAcoustic feedback audio signal; Another is highly sensitive microphone (main microphone), and it can obtain closely simultaneouslyEnd acoustic feedback audio signal and far-end speech audio signal;

C. in order to ensure acoustic feedback elimination performance, necessary choose reasonable reference microphone and main microphone. ReferenceWithin the difference that microphone and main microphone pick up the intensity of acoustic feedback audio signal need be controlled at 3db; ReferenceMore than the difference that microphone and main microphone pick up the intensity of voice audio signals need be greater than 20db.

D. in order to improve system signal noise ratio, eliminate fast acoustic feedback signal, select sample frequency 48khz, differentiateRate is more than or equal to the AD converter of 20bit.

(2) the Audio Signal Processing stage

A. the impulse response of practical matter audio feedback path often has sparse characteristic, the tap coefficient of FIR wave filterConcentration of energy is within an arrowband scope, and the energy of other tap coefficients is very little (is referred to as the non-tap that enlivensCoefficient), the present invention adopts PNLMS self adaptation elimination algorithm convergence speedup speed;

B.FIR filter order is more than or equal to 128, and typical occasion adopts 256;

C. process window slip interval and equal 6 sampled points, every 6 sampled points complete adaptive algorithm fortune one timeCalculate;

D. the pickup of main microphone output d (n)=s (n)+f (n) * x (n), wherein s (n) is equivalent far-end speech input,F (n), n=0,1 ..., N-1 is the unit impulse response of equivalent sound feedback FIR wave filter, x (n) is that equivalence is with reference to wheatThe input of gram wind;

E. apply the feedback acoustic feedback elimination algorithm of PNLMS and eliminate the acoustic feedback composition in main microphonef(n)*x(n)；

F. in order to reduce the correlation of acoustic feedback signal and former voice signal, the output after self-adaptive processing is passed throughAfter the pure time delay of 240～480 sampled points, export.

(3) audio frequency output and power amplification

A. through the DAB process DA converter of DSP self-adaptive processing, anti-aliasing analog filter is changedBecome simulated audio signal;

B. adopt T class power amplification circuit to carry out Audio power amplifier;

Two audio signal sample microphones, audio signal sample, DSP process, audio signal is exported and powerThe electronic circuits such as amplification, power circuit, and within loudspeaker are arranged on same sound box bodies together, be integrally formedChange feed forward type acoustic feedback restraining device.

The substantial effect that the present invention brings is, has higher gain and good robustness, can accurately eliminateEnvironmental noise, prevent to clamor and cry, processing speed is fast, applied widely.

Brief description of the drawings

Fig. 1 is the structured flowchart of sound reinforcement system;

Fig. 2 is the Mathematical Modeling of a kind of self adaptation acoustic feedback Restrainable algorithms of the present invention;

Fig. 3 is the Mathematical Modeling of a kind of integrated feed forward type acoustic feedback Restrainable algorithms of the present invention;

Fig. 4 is a kind of feed forward type adaptive algorithm of the present invention;

Fig. 5 is the integrated feed forward type acoustic feedback of one of the present invention restraining device structure chart;

In figure: 101, pickup link, 102, public address link, 103, sounding link, 104, acoustic feedback ringJoint, 201, amplifying element, 202, acoustic feedback estimation function, 203, audio feedback path transfer function, 301,Far-end speech signal is to the transfer function between the output of main microphone pickup, and 302, far-end speech signal is to referenceTransfer function between microphone pickup output, 303, self-adaptive processing algorithm, 304, DA conversion, postpositionThe equivalent transfer function of voltage amplification and power amplification, 305, loudspeaker export the acoustic feedback transmission between audio amplifier toFunction, 306, the transfer function of main microphone to audio amplifier near-end voice signals pickup, 307, reference microphoneTo the transfer function of audio amplifier near-end voice signals pickup, 401, the FIR acoustic feedback on N rank offsets digital filter,402, delayer, 501, square sound box casing, 502, reference microphone, 503, main microphone, 504,Built-in loudspeaker, 505, pcb board and related electronic devices.

Detailed description of the invention

Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

Embodiment: Fig. 1 is the structured flowchart of sound reinforcement system, and it is mainly by pickup link 101, public address link102, the acoustic feedback link 104 that sounding link 103 and space exist forms. Due to the existence of acoustic feedback, wholeIndividual system has formed a closed-loop system. When loop gain is greater than 1, meet positive feedback condition simultaneously and produce certainlyInduced Oscillation, acoustic feature is to produce the audio signal of uttering long and high-pitched sounds.

Fig. 2 is the Mathematical Modeling of self adaptation acoustic feedback Restrainable algorithms, its objective is the transmission to audio feedback pathFunction 203 is estimated, estimates that 202 obtain offsetting acoustic feedback signal, as f'(n by acoustics) when=f (n),Eliminate acoustic feedback completely; Work as f'(n)-f (n)=kf (n), when 0≤k < 1, reduce acoustic feedback amount, loop is carriedRise be approximately equal to-20logk of gain.

Fig. 3 is the Mathematical Modeling of the integrated feed forward type acoustic feedback Restrainable algorithms that adopts of the present invention, and wherein s (n) isFar-end speech signal; K_master(z) (301) are that far-end speech signal is to the biography between main microphone pickup outputDelivery function, K_ref(z) (302) are that far-end speech signal is to the transfer function between the output of reference microphone pickup;F (z) (305) is that loudspeaker export the acoustic feedback transfer function between audio amplifier, F to₁(z) (306) are main microphonesTo the transfer function of audio amplifier near-end voice signals pickup, F₂(z) (307) are that reference microphone is to audio amplifier near-end languageThe transfer function of tone signal pickup; D (n) is the summation of main microphone to near-end and far-end speech pickup, and x (n) isThe summation of reference microphone to near-end and far-end speech pickup. Y (n) is the defeated of feed forward type self-adaptive processing algorithmGo out; G (z) (304) is DA output, the equivalent transfer function of rearmounted voltage amplification and power amplification, g (n)It is final output audio signal.

In order to ensure that self adaptation elimination algorithm can effectively offset acoustic feedback signal, main microphone must be selected highSensitivity microphone, reference microphone must select closely to say formula microphone, while selecting microphone is installed, requires twoThe pickup characteristic of microphone must meet following condition:

20log||K_master(z)||²-20log||K_ref(z)||²≥20dB

|20log||F₁(z)||²-20log||F₂(z)||²|≤3dB

In the time meeting above-mentioned condition

X(z)＝S(z)·K_ref(z)+F(z)·F₂(z)·Y(z)≈F"(z)·Y(z)

D(z)＝S(z)·K_master(z)+F(z)·F₁(z)·Y(z)≈S(z)+F(z)·F₁(z)·Y(z)

$F^{\&prime;}\left(z\right)\stackrel{\mathrm{\&Delta;}}{=}{F}^{\&prime;}\left(z\right)\&CenterDot;F\left(z\right)\&DoubleRightArrow;D\left(z\right)=S_1\left(z\right)+F^{\&prime;}\left(z\right)\&CenterDot;Y\left(z\right)$

$H\left(z\right)\stackrel{\mathrm{\&Delta;}}{=}\frac{F^{\&prime;\&prime;}\left(z\right)}{F^{\&prime;}\left(z\right)}$

D(z)＝S(z)+H(z)·X(z)

$d\left(n\right)=s\left(n\right)+\underset{k=0}{\overset{N-1}{\&Sigma;}}h\left(k\right)\&CenterDot;x(n-k)$

Self adaptation elimination algorithm (303) will be by main microphone pickup signalInX (n) relevant portion, i.e. the acoustic feedback of reference microphone feedback composition is eliminated.

G (z) (304) is DA conversion output, and letter is transmitted in the equivalence of the links such as anti-aliasing filter and power amplificationNumber, its characteristic is a fixed gain substantially.

Fig. 4 is feed forward type adaptive algorithm. X (n) is the acoustic feedback signal that reference microphone is picked up, and d (n) comprisesFar-end speech signal and near-end acoustic feedback signal composition. 401 is the FIR acoustic feedback counteracting numerals on N rankWave filter.

WhenFrequency characteristic approachTime, can eliminateAcoustic feedback signal.

Consider the sparse property of audio feedback path, the present invention adopts PNLMS algorithm to estimate actual reference detection soundFeedback path H (z), recursive algorithm is as follows:

x(n)＝[x(n)x(n-1)…x(n-N+1)]^T

$\hat{f}\left(n\right)={\hat{h}}^T\left(n\right)x\left(n\right)$

$e\left(n\right)=d\left(n\right)-\hat{f}\left(n\right)$

$h(n+1)=h\left(n\right)+\frac{\mathrm{\&mu;}G(n+1)}{\mathrm{\&alpha;}+x^T\left(n\right)G(n+1)x\left(n\right)}e\left(n\right)x\left(n\right)$

Wherein:

G(n+1)＝diag{g₁(n+1),g₂(n+1)…,g_N(n+1)}

$\begin{array}{c}{\mathrm{\&gamma;}}_{min}(n+1)=\mathrm{\&rho;}\&CenterDot;\max \{{\mathrm{\&delta;}}_p,|{\hat{h}}_1\left(n\right)|,\\ \left|{\hat{h}}_2\left(n\right)\right|,\mathrm{...},\left|{\hat{h}}_N\left(n\right)\right|\}\end{array}$

${\mathrm{\&gamma;}}_k(n+1)=\max \{{\mathrm{\&gamma;}}_{\min }(n+1),|{\hat{h}}_k\left(n\right)\left|\right\}$

$g_k(n+1)=\frac{{\mathrm{\&gamma;}}_k(n+1)}{\frac{1}{N}\underset{i=1}{\overset{N}{\&Sigma;}}{\mathrm{\&gamma;}}_i(n+1)},1\&le;k\&le;N$

In above-mentioned formula, μ is step factor, and α is very little positive integer. ρ is one affects global convergence speedThe important parameter of degree is larger on the impact of initial convergence speed. ρ is larger, and initial convergence speed is slower, lessρ can realize initial convergence speed faster, but also can cause slower after-stage convergence rate simultaneously. ρGeneral value is 5/N.

402 is pure delayers, in order to reduce the correlation between acoustic feedback signal and far-end spoken sounds,Non-stationary according to voice signal, time delay sampled point can be selected 240～480 points, is equivalent to 5ms～10msTime delay.

Fig. 5 is the integrated feed forward type acoustic feedback restraining device structure chart of realizing above-mentioned algorithm. The 501st, square soundBox body; 502 are mounted in the reference microphone of sound box front, special detection acoustic feedback signal, 503Be mounted in the main microphone of sound box front, detection acoustic feedback signal and far-end speech signal, the 504th, inPut loudspeaker, the 505th, realize pcb board and the related electronic devices of the electronic circuit part of above-mentioned algorithm.

Specific embodiment described herein is only to the explanation for example of the present invention's spirit. Under the present inventionThose skilled in the art can make various amendments or supplement or adopt described specific embodimentAlternative by similar mode, determine but can't depart from spirit of the present invention or surmount appended claimsThe scope of justice.

Although more used the terms such as self adaptation, feedback, transfer function herein, do not got rid of useThe possibility of other term. Use these terms to be only used to describe more easily and explain of the present inventionMatter; They are construed to any additional restriction is all contrary with spirit of the present invention.

Claims (6)

1. a self adaptation acoustic feedback removing method, is characterized in that, comprises the following steps:

One, main microphone picks up audio signal d (n), and d (n) comprises far-end speech signal and near-end acoustic feedback signalComposition, reference microphone is picked up acoustic feedback signal x (n);

Two, d (n) and x (n) are adopted to self adaptation elimination algorithm, are output as e (n), be specially:

WhenFrequency characteristic approach $H\left(z\right)=\frac{F^{\&prime;\&prime;}\left(z\right)}{F^{\&prime;}\left(z\right)}$ Time, can eliminateAcoustic feedback signal;

Adopt PNLMS algorithm to estimate that actual reference detects audio feedback path H (z), recursive algorithm is as follows:

x(n)＝[x(n)x(n-1)…x(n-N+1)]^T

$\hat{f}\left(n\right)={\hat{h}}^T\left(n\right)x\left(n\right)$

$e\left(n\right)=d\left(n\right)-\hat{f}\left(n\right)$

$h(n+1)=h\left(n\right)+\frac{\mathrm{\&mu;}G(n+1)}{\mathrm{\&alpha;}+x^T\left(n\right)G(n+1)x\left(n\right)}e\left(n\right)x\left(n\right)$

Wherein:

G(n+1)＝diag{g₁(n+1),g₂(n+1)…,g_N(n+1)}

${\mathrm{\&gamma;}}_k(n+1)=max\{{\mathrm{\&gamma;}}_{min}(n+1),|{\hat{h}}_k\left(n\right)\left|\right\}$

$g_k(n+1)=\frac{{\mathrm{\&gamma;}}_k(n+1)}{\frac{1}{N}\underset{i=1}{\overset{N}{\&Sigma;}}{\mathrm{\&gamma;}}_i(n+1)},1\&le;k\&le;N$

In above-mentioned formula, μ is step factor, and α is positive integer, and ρ is the parameter that affects global convergence speed;Refer to the unit impulse response of echo path estimation FIR wave filter; N refers to the rank of echo path FIR wave filterInferior; T is transposition symbol;Refer to echo estimation output;Refer to that echo path estimates the of FIR wave filterK estimation coefficient; F " (z) ≈ F (z) F₂(z)，F'(z)≈F(z)·F₁(z), F (z) is that loudspeaker export between audio amplifierAcoustic feedback transfer function; F₁(z) be the transfer function of main microphone to audio amplifier near-end voice signals pickup, F₂(z)The transfer function of reference microphone to audio amplifier near-end voice signals pickup;

Three, e (n) is carried out to digital-to-analogue conversion and power amplification;

Four, by loudspeaker output audio signal.

2. a kind of self adaptation acoustic feedback removing method according to claim 1, is characterized in that stepTwo e (n) outputs to digital-to-analogue conversion after time delay module again.

3. a kind of self adaptation acoustic feedback removing method according to claim 1 and 2, is characterized in that,Within the difference that reference microphone and main microphone pick up the intensity of acoustic feedback audio signal need be controlled at 3db;The difference that reference microphone and main microphone pick up the intensity of voice audio signals need be greater than 20db.

4. a kind of self adaptation acoustic feedback removing method according to claim 1 and 2, is characterized in that,Process window slip interval and equal 6 sampled points, every 6 sampled points complete an adaptive algorithm computing.

5. a kind of self adaptation acoustic feedback removing method according to claim 4, is characterized in that, AD turnsThe sample frequency of parallel operation is 48KHz, and resolution ratio is not less than 20bit.

6. a kind of self adaptation acoustic feedback removing method according to claim 5, is characterized in that, FIR filterRipple device order is not less than 128.