58results about How to "Good echo cancellation" patented technology

The invention discloses an adaptive echo cancellation method adopting memory proportionate affine projection and based on M-estimation. The adaptive echo cancellation method comprises the following steps: A, a remote signal is sampled; B, in echo estimation, a filter input vector X(n) passes through an adaptive filter and an output value y(n), namely an echo estimation value y(n), is obtained and equal to XT(n)w(n); C, in echo cancellation, a near-end signal d(n) with the echo, acquired by a near-end microphone and the output value y(n) of the adaptive filter are subjected to subtraction operation, and a return signal, namely a residual signal e(n), is returned to a far end and equal to d(n)-y(n); D, in updating of a tap weight vector of the filter, the method adopting memory proportionate affine projection and based on M-estimation is used and the tap weight vector w(n+1) of the adaptive filter at next moment n+1 is calculated and equal to w(n)+mu P(n)(UT(n)P(n)+delta IK)-1 psi [E(n)]; E, n is set to be n+1, and the steps B, C and D are repeated until a call is ended. The method has a good cancellation effect on an acoustic echo of a communication system and is high in convergence speed and small in steady-state error.

The invention discloses a low complexity phone echo self-adaption eliminating method. The method mainly comprises the steps of: A, far-end signal filtration: obtaining a large-step filtering value y1(n) and a small-step filtering value y2(n); B, convex combination: completing the convex combination of the large-step filtering value y1(n) and the small-step filtering value y2(n) so as to obtain a combined filtering value y(n), wherein y(n)=lambda(n)y1(n)+(1-lambda(n))y2(n); C, echo cancellation: completing the subtraction of a near-end signal d(n) which has echo and is picked by a near-end microphone and the combined filtering value y(n), and then sending the signal back to a far end, wherein a return signal is a total residual signal e(n), and e(n)=d(n)-y(n); D, updating the weight coefficient of filtering device taps; E, updating the weight of the filtering device, i.e. updating a hybrid parameter a(n) through a formula which is simplified by a sign function; F, defining the weight of the filtering device; and G, setting n=n+1, and repeating the steps of A, B, C, D, E and F until the conversation is over. The method has strong identification capability to sparse telephone communication systems, and particularly has quick convergence rate and small steady state error in a transition period; an echo eliminating effect is good; and moreover, the method is low in calculation complexity, low in hardware cost and easy to implement.

An improved convex combination decorrelation proportionate self-adaption echo cancellation method comprises the steps that first, far-end signal filtering is carried out, the input vector X(n) of a convex combination self-adaption echo cancellation filter is formed by the discrete value of a far-end signal, and after filtering is carried out on the input vector, a large-step-length filtering value y1(n) and a small-step-length filtering value y2(n) are obtained; second, decorrelation operation is carried out on the input vector X(n), the result of decorrelation operation serves as the weight coefficient updating direction vector Z(n) of the convex combination self-adaption echo cancellation filter; third, convex combination is carried out, the large-step-length filtering value y1(n) and the small-step-length filtering value y2(n) are subjected to convex combination through weight lambda (n), and a combination filter value y(n) is obtained; fourth, echo cancellation is carried out, the combination filter value y(n) is subtracted from a near-end signal d(n) with an echo, and the subtracted near-end signal d(n) is fed back to the far end; fifth, a filter tap weight coefficient is updated; sixth, the weight of the filter is updated; seventh, the weight of the filter is limited; eighth, n is made to be equal to n+1, the first step to the seventh step are repeated till a conversation is over. According to the method, a high rate of convergence can be obtained, the steady state error can be small, and a good anti-jamming capability is achieved.

The invention relates to an exponential function echo cancellation method based on one-norm and zero-attractor, and discloses a zero-norm sub-band echo cancellation method. The method comprises the following steps: A, remote signal sampling; B, echo signal estimation; C, echo signal cancellation; D, filter tap weight coefficient updating; and E, assuming that n is equal to n+1, and repeating steps B, C and D to achieve real-time echo cancellation. According to the method disclosed by the invention, a one-norm of a weight coefficient vector is adopted in the derivation of a weight coefficient vector updating formula, the norm relates to FORMULA, wherein gamma is a proportion parameter of the one-norm of the weight coefficient vector, zero-attractor Rho (n) is obtained from the derivation, Rho (n) is equal to b.sgnW(n), which means that the speed that the weight coefficient vector is updated to zero is faster under sparse systems; a method for taking an exponential function as a cost function is adopted during weight update, and thus the cost function is changed to FORMULA when updating the weight coefficient vector, and a new step length factor is introduced to ensure that output signals of a filter can obtain faster convergence and lower steady state maladjustment under Gaussian signals and the sparse systems.

The invention discloses a convex combination adaptive echo cancellation method for an affine projection sign subband adaptive filter. The method mainly comprises the following steps: A, far-end signal filtering: performing far-end signal filtering to obtain the output vector Y1(n) of a large-step-length filter and the output vector Y2(n) of a small-step-length filter; B, convex combination: performing convex combination on the output vector Y1(n) and the output vector Y2(n) of the large-step-length filter and the small-step-length filter to obtain a combination filtering value Y(n) (Y(n)= lambda(n)Y1(n)+(1-lambda(n))Y2(n)), and performing convex combination on the tap weight vector W1(n) of the large-step-length filter and the tap weight vector W2(n) of the small-step-length filter to obtain a total filter group tap weight vector W(n) (W(n)=lambda(n)W1(n)+(1-lambda(n))W2(n); C, echo cancellation: subtracting the combination filter value Y(n) from an echo-carrying affine projection near-end signal D(n) picked up by a near-end microphone to obtain a net signal E(n) (E(n)=D(n)-Y(n)), and transmitting the net signal back to a far end; D, updating the tap weight coefficients of the filters; E, weight update of the filters: updating hybrid parameters a(n); and F, iteration: repeating the steps above under the condition that n=n+1 till the end of a conversation. The method has the advantages of high cancellation capability on the acoustic echo of a sparse communication system, high convergence speed, small steady-state error and good echo cancellation effect.

The invention discloses an echo cancellation method based on block-sparse proportionate reuse weight coefficient affine projection. The method mainly comprises following steps of A, filtering a far-end signal; obtaining an output value y (n) from a filter input vector X (n) through a self-adaptive filter; B, cancelling an echo; subtracting the output value y (n) by near-end signal d (n) which is picked up by a near-end microphone at a current moment and is equipped with the echo, thus obtaining an error as a residual signal e (n), wherein e (n) is equal to d (n)-y(n); sending back to a far-end; C, updating a filter tapping weight vector; D, assuming that n is equal to n+1, repeating steps A, B and C until finishing a call. According to the method, the echo elimination capability to the voice communication of a sparse system is high; the rate of convergence is high; the steady state error is low; and the echo elimination effect is good.

The invention provides a robust convex combination type adaptive phone echo canceling method. The method comprises the main steps of A, filtering a far-end signal to obtain quick adaptive filter output y1(n) and low-error filter output y2(n); B, convex combination, namely, performing convex combination for the outputs of the two filters to obtain output y(n) of convex combination filters; c, canceling echo, namely, receiving a near-end signal d(n) with echo by a near-end microphone, subtracting y(n) and then transmitting to a far end as an echo-canceled near-end signal e(n); D, updating tap weight coefficients of the filters; E, updating the weights of the filter, and updating the hybrid parameter a (n) through a formula with sign functions; F, limiting the weight of the filters; G, enabling n to be n+1, and repeating A, B, C, D, E and F until the call is ended. The method is high in convergence rate, low in steady-state error, high in interference resistance, good in echo canceling effect, and maintains good working state under the condition of double-talk of a phone.

The present application discloses an echo cancellation method and device. The echo cancellation method includes: updating a parameter of a cloud echo cancellation system by using a first reference signal at a first moment, wherein the first reference signal is a voice synthesizing result after the cloud server responds to a first user instruction; using a target reference signal to perform echo cancellation on a microphone signal through the cloud echo cancellation system at a second moment after the first moment if the microphone signal and the target reference signal uploaded by the client are received. Since cloud echo cancellation can use a more complex echo cancellation algorithm than local and pre-estimate echo cancellation filter parameters, a better echo cancellation effect can beachieved when echo cancellation is performed on the microphone signal.

The invention discloses a minimum mean cubic absolute value echo elimination method of convex combination. The method comprises the following steps: A, performing far-end signal filtering to obtain alarge step length filtering value y1(n) and a small step length filtering value y2(n); B, performing convex combination: performing convex combination on the large step length filtering value y1(n) and the small step length filtering value y2(n) to obtain a combined filtering value y(n); C, performing echo cancellation; D, performing filter tap weight coefficient update: respectively performing tap weight coefficient update of each of a large step length filter and a small step length filter based on the minimum mean cubic absolute value algorithm; E, performing weight update of the filter; F,performing weight limitation of the filter; G, enabling n=n+1, repeating the steps A, B, C, D, E and F until the call is ended. The cost function for the tap weight coefficient update of each of thesmall and large filters is the mean cubic absolute value of the total residual signal e(n), the error is amplified, the operational precision is high; through the fast convergence of the large step length filter and the small steady state error of the small step length filter, and the steady state error is small, the convergence speed is fast, and the echo cancellation effect is good.

A zero-norm set membership affine projection adaptive echo cancellation method based on weight vector reuse comprises the following steps: A, remote signal sampling; B echo estimation: obtaining an output value y(n) at a current time n through an adaptive filter based on an input vector X(n) of the adaptive filter at the current time n, wherein y(n) is the estimated value of echo, and y(n)=X<T>(n)*w(n); C, echo cancellation: subtracting the output value y(n) of the adaptive filter at the current time n from a near-end signal d(n) with echo at the current time n picked up by a near-end microphone to get a residual signal e(n), wherein e(n)=d(n)-y(n), and transmitting the residual signal back to a remote end; D, filter tap weight vector updating: getting a tap weight vector w(n+1) (Please see the description for the expression) of the filter at next time n+1 using a zero-norm set membership affine projection method based on weight vector reuse; and E, letting n=n+1, and repeating the steps B, C and D until the end of the call. The method has the advantages of good cancellation effect for acoustic echo of a communication system, high convergence rate, and small steady-state error.

The invention discloses a self-adaptive sound echo cancellation method based on frequency domain Kalman filtering. The method comprises the following specific steps: (1) collecting and caching a near-end microphone signal and a reference signal, and converting an analog signal into a digital signal through analog-digital conversion; (2) carrying out discrete Fourier transform on the digital signal, and calculating an estimated echo signal; (3) carrying out iteration according to an iterative formula of the frequency domain Kalman filter, and calculating a filter coefficient of the next frame according to an improved formula; and (4) carrying out inverse discrete Fourier transform on echo signal estimation in the frequency domain to obtain echo signal estimation of the time domain, and subtracting the echo signal estimation of the time domain from the near-end microphone signal, thus obtaining the signal after the sound echo cancellation processing. According to the method disclosed bythe invention, the optimal solution can be converged even under the condition that the order of a self-adaptive filter is insufficient or the system does not meet the causality, and a good echo cancellation effect is obtained.

The invention provides a normalization sub-band self-adaptive echo elimination method of biased compensation. The method comprises the following steps of: A, obtaining a sub-band signal; B, carrying out self-adaptive filtering, obtaining an mth output value ym(k) at the sampling moment n=kM through a self-adaptive filter from an mth input vector Km(k) of the self-adaptive filter at the sampling moment n=kM, wherein ym(k) is shown in the specification; C, carrying out echo cancelling, subtracting the mth filter output value ym(k) at the sampling moment n=kM from a kth time segment extraction value d<->m(k) of an mth near-end sub-band signal to obtain an mth sub-band residual em(k) at the sampling moment n=kM, wherein em(k)=d<->m(k)-ym(k); D, carrying out filter tap weight vector updating, using a normalization sub-band method of biased compensation to obtain a tap weight vector w(k+1) of the self-adaptive filter at the moment n=(k+1)M, wherein w(k+1) is shown in the specification; and E, for k=k+1, repeating B, C and D steps until the conversation ends. The method has the advantages that the acoustic echo elimination effect of a communication system is good, the convergence speed is high, and the steady-state error is small.

A method for eliminating combined step length proportional affine projection echo comprises the following steps of A, acquiring a sound signal x(n) transmitted from a far end at a telephone loudspeaker and acquiring an expected signal d(n) at a telephone microphone; B, using the sound signal x(n) as an input of an adaptive filter, and using the output of the adaptive filter as an estimated value d<^>(n) of an echo signal; C, performing echo offset, namely using an error signal e(n) obtained through subtracting d<^>(n) from d(n) as a clean signal after echo offset and transmitting the error signal to the remote end; D, calculating an error vector of the adaptive filter; E, calculating a weight updating increment deltaw(n) of the adaptive filter; F, calculating a combined step length mu(n); G, updating the weight vector w(n+1)=w(n)+mu(n)deltaw(n) of the adaptive filter; and H, setting n=n+1, repeating the steps A, B, C, D, E, F, G until the calling is finished. The method has advantages of high convergence speed and low steady-state error.

An adaptive echo cancellation method based on correlation entropy is disclosed. The method comprises the following steps: A, collecting far-end signals, sampling far-end signals transmitted from far-end to obtain the discrete value x (n) of far-end input signals at the current time n, and the filter input signal vector is x (n) = [x (n), x (n-1),..., x (n-L+1)] T; B, sampling far-end signals transmitted from far-end to obtain the discrete value x (n) of far-end input signals at the current time n. B, estimate an echo signal, passing an input signal vector x (n) of that current time n through an adaptive filt, and outputting a value y (n) thereof, that is, an estimated value of the echo signal; C, canceling the echo, sampling with a near-end microphone to obtain the near-end signal d (n) ofthe current time n of the echo return, subtracting the estimated value y (n) of the echo signal; D, updating the tap weight coefficients of the filter, and calculating a tap weight vector w (n+1), w(n+1) = w (n) + Mu(n) U (n) (UT (n) U (n)) of n+1 at the next time of the filter; 1E(n)-C (n); E, let n=n+1, repeating the process of A, B, C, D, E until the end of the call. This method can obtain faster convergence rate and lower steady-state error, and the echo cancellation effect is good.

The invention discloses a self-adaptive echo cancellation method based on time-varying parameters. The adaptive echo cancellation method comprises the following steps: A, far-end signal sampling; B, echo signal estimation: obtaining an output value y (n) from the filter input vector X (n) through an seld-adaptive filter, namely, an echo estimation value y (n), y (n) = WT (n) X (n); C, echo signalelimination: subtracting the near-end signal d (n) with echo picked up by the near-end microphone from the output value y (n) of the self-adaptive filter, and sending the subtracted near-end signal d(n) and the subtracted output value y (n) back to the far end, the returned signal being a residual signal e (n), and e (n) = d (n)-; y (n); D, updating a tap weight vector of the filter; estimating aresidual signal square sequence Ae (n) in the window according to the current moment n, obtaining a smooth square value of the residual signal, further obtaining an adjusting parameter (n) of a time-varying proportional control factor and a time-varying proportional control factor gk (n), and finally obtaining an adaptive filter tap weight W (n + 1) at the next moment; and E, enabling n to be equal to n + 1, and repeating the steps A, B, C and D until the conversation is finished. The method has the advantages of good elimination effect on acoustic echoes of a communication system, high convergence speed and small steady-state error.

The present invention discloses an affine projection symbol echo cancellation method based on orthogonal transformation. The method mainly comprises the steps of A acquiring a voice signal x(n) transmitted from a far-end and a desired signal d(n) collected by a near-end microphone; B calculating the output y(n) of an adaptive filter; C carrying out the echo cancellation, namely, taking an error signal e(n) obtained by subtracting y(n) from d(n) as a clean signal after the echo cancellation to transmit to the far-end; D orthogonally transforming an input matrix X(n) of the adaptive filter into a decorrelation matrix U(n); E updating a tap weight vector of the adaptive filter; F defining n=n+1, and repeating the steps of A, B, C, D and E until the conversation is ended. The method has the advantages of fast convergence speed and low steady-state error except having the good robustness for the doubletalk.

The invention provides a robust convex combination type adaptive phone echo canceling method. The method comprises the main steps of A, filtering a far-end signal to obtain quick adaptive filter output y1(n) and low-error filter output y2(n); B, convex combination, namely, performing convex combination for the outputs of the two filters to obtain output y(n) of convex combination filters; c, canceling echo, namely, receiving a near-end signal d(n) with echo by a near-end microphone, subtracting y(n) and then transmitting to a far end as an echo-canceled near-end signal e(n); D, updating tap weight coefficients of the filters; E, updating the weights of the filter, and updating the hybrid parameter a (n) through a formula with sign functions; F, limiting the weight of the filters; G, enabling n to be n+1, and repeating A, B, C, D, E and F until the call is ended. The method is high in convergence rate, low in steady-state error, high in interference resistance, good in echo canceling effect, and maintains good working state under the condition of double-talk of a phone.

The invention relates to a normalized sub-band adaptive echo elimination method based on M estimation. The method comprises steps that A, a sub-band signal is acquired; B, adaptive filtering, an mth filter output value ym(k) at the sampling time n=kM is acquired through an adaptive filter on the basis of an mth adaptive filter input vector Xm(k) at the sampling time n=kM, and an equation described as the specification is acquired; C, echo cancellation, a kth time segment extraction value d<->m(k) of an mth near-end sub-band signal subtracts the mth filter output value ym(k) at the sampling time n=kM to acquire an mth sub-band residual error em(k) at the sampling time n=kM, and an equation described as the specification is acquired; D, a filter tap weight vector is updated, a normalized sub-band method based on M estimation is utilized, a tap weight vector w(k+1) of the adaptive filter at the time n=(k+1)M is acquired, and an equation described as the specification is acquired; and E, k=k+1 is made, the steps of B, C and D are repeated till conversation is over. Through the method, the acoustic echo elimination effect for a communication system is good, a convergence speed is fast, and a stable state error is small.

The invention discloses a proportional affine projection echocancellation method of convex combination coefficient differences. The method includes the following steps: A. far-end signals sampling and filtering, A1. a far-end dispersion input signal constitutes an input vector X (n) of a convex combination adaptive filtering echocancellation filter; A2. inputting a filter to the vector X (n) to obtain filtering values y1 (n) and y2 (n) of a large step length and a small step length; B. echo offsetting, subtracting a near-end signal d (n) and a combination filter output value y (n) to obtain a total residual signal e (n) after echocancellation, and transmitting the total residual signal e (n) to a far end; C. conducting convex combination, conducting convex combination on the output value y1 (n) and y2 (n) of the large step length and the small step length through a weight [lambda] to obtain a combination output value y (n), and conducting convex combination on tap weight coefficients W1 (n) and W2 (n) through the weight [lambda] to obtain a tap weight coefficient W (n) of a tap filter; D. updating a tap weight vector of a filter; E. updating the weight of the filter; F. limiting the weight of the filter; G. repeating the steps of A, B, C, D, E, F, thus realizing echocancellation. The method has strong identification capability, rapid convergence, strong tracking capability, low stability error rate, and excellent effects of echocancellation.

A microphone array device including microphone capsules and at least one processing unit configured to receive output signals of the microphone capsules, dynamically steer an audio beam based on the received output signal of the microphone capsules, and generate and provide an audio output signal based on the received output signal of the microphone capsules. The processing unit is configured to operate in a dynamic beam mode where at least one focused audio beam is formed that points towards a detected audio source and in a default beam mode where a broader audio beam is formed that covers substantially a default detection area. The microphone array may be incorporated into a conference system.

The invention provides an echo cancellation method and device, electronic equipment and a readable storage medium, and aims to improve the echo cancellation effect. The method comprises the following steps: acquiring a to-be-processed mixed voice signal and a to-be-processed far-end voice signal; inputting the to-be-processed mixed voice signal into a pre-trained network model to obtain a first feature vector; inputting a far-end voice signal to be processed into the network model to obtain a second feature vector; inputting the first feature vector and the second feature vector into a pre-trained attention model to obtain a third feature vector; and obtaining a near-end voice signal after echo cancellation according to the first feature vector and the third feature vector. The echo cancellation method and device, the electronic equipment and the readable storage medium provided by the invention have a better echo cancellation effect.

A kind of anti-impact noise interference M estimate decorrelation proportional adaptive echo cancellation method, its steps are as follows: A, far-end signal collection, sampling the far-end signal that far-end transmits, can obtain the far-end of current moment n The discrete value x(n) of the input signal, its filter input signal vector is x(n)=[x(n),x(n‑1),...,x(n‑L+1)] T ; B, echo signal estimation, pass the input signal vector x (n) of time n through the adaptive filter, and its output value y (n) is the estimated value of the echo signal; C, the decorrelation operation of the input signal, calculate the correlation coefficient a(n), obtain the update vector z(n) of the adaptive filter tap weight vector w(n); D, echo cancellation, use the near-end microphone sampling to obtain the near-end signal d(n) of the current moment n with echo ) minus the estimated value y(n) of the echo signal; E, filter tap weight coefficient update, calculate the evaluation function ψ(e(n)) updated by the tap weight vector w(n) of the filter at the current moment n and update Step size matrix G(n); F, let n=n+1, repeat the above-mentioned process of A, B, C, D, E until the end of the call.

The invention discloses a low complexity phone echo self-adaption eliminating method. The method mainly comprises the steps of: A, far-end signal filtration: obtaining a large-step filtering value y1(n) and a small-step filtering value y2(n); B, convex combination: completing the convex combination of the large-step filtering value y1(n) and the small-step filtering value y2(n) so as to obtain a combined filtering value y(n), wherein y(n)=lambda(n)y1(n)+(1-lambda(n))y2(n); C, echo cancellation: completing the subtraction of a near-end signal d(n) which has echo and is picked by a near-end microphone and the combined filtering value y(n), and then sending the signal back to a far end, wherein a return signal is a total residual signal e(n), and e(n)=d(n)-y(n); D, updating the weight coefficient of filtering device taps; E, updating the weight of the filtering device, i.e. updating a hybrid parameter a(n) through a formula which is simplified by a sign function; F, defining the weight of the filtering device; and G, setting n=n+1, and repeating the steps of A, B, C, D, E and F until the conversation is over. The method has strong identification capability to sparse telephone communication systems, and particularly has quick convergence rate and small steady state error in a transition period; an echo eliminating effect is good; and moreover, the method is low in calculation complexity, low in hardware cost and easy to implement.

An improved convex combination decorrelation proportionate self-adaption echo cancellation method comprises the steps that first, far-end signal filtering is carried out, the input vector X(n) of a convex combination self-adaption echo cancellation filter is formed by the discrete value of a far-end signal, and after filtering is carried out on the input vector, a large-step-length filtering value y1(n) and a small-step-length filtering value y2(n) are obtained; second, decorrelation operation is carried out on the input vector X(n), the result of decorrelation operation serves as the weight coefficient updating direction vector Z(n) of the convex combination self-adaption echo cancellation filter; third, convex combination is carried out, the large-step-length filtering value y1(n) and the small-step-length filtering value y2(n) are subjected to convex combination through weight lambda (n), and a combination filter value y(n) is obtained; fourth, echo cancellation is carried out, the combination filter value y(n) is subtracted from a near-end signal d(n) with an echo, and the subtracted near-end signal d(n) is fed back to the far end; fifth, a filter tap weight coefficient is updated; sixth, the weight of the filter is updated; seventh, the weight of the filter is limited; eighth, n is made to be equal to n+1, the first step to the seventh step are repeated till a conversation is over. According to the method, a high rate of convergence can be obtained, the steady state error can be small, and a good anti-jamming capability is achieved.

The invention discloses an adaptive echo cancellation method adopting memory proportionate affine projection and based on M-estimation. The adaptive echo cancellation method comprises the following steps: A, a remote signal is sampled; B, in echo estimation, a filter input vector X(n) passes through an adaptive filter and an output value y(n), namely an echo estimation value y(n), is obtained and equal to XT(n)w(n); C, in echo cancellation, a near-end signal d(n) with the echo, acquired by a near-end microphone and the output value y(n) of the adaptive filter are subjected to subtraction operation, and a return signal, namely a residual signal e(n), is returned to a far end and equal to d(n)-y(n); D, in updating of a tap weight vector of the filter, the method adopting memory proportionate affine projection and based on M-estimation is used and the tap weight vector w(n+1) of the adaptive filter at next moment n+1 is calculated and equal to w(n)+mu P(n)(UT(n)P(n)+delta IK)-1 psi [E(n)]; E, n is set to be n+1, and the steps B, C and D are repeated until a call is ended. The method has a good cancellation effect on an acoustic echo of a communication system and is high in convergence speed and small in steady-state error.

A kind of self-adaptive echo cancellation method based on the set member of correlation entropy, its steps are as follows: A, far-end signal acquisition, the far-end signal that far-end transmits is sampled, can obtain the discreteness of the far-end input signal of current moment n Value x(n), whose filter input signal vector is x(n)=[x(n),x(n‑1),...,x(n‑L+1)] T ; B, echo signal estimation, pass the input signal vector x (n) of the current moment n through the adaptive filter, and its output value y (n) is the estimated value of the echo signal; C, echo cancellation, obtain with near-end microphone sampling Subtract the estimated value y(n) of the echo signal from the near-end signal d(n) at the current moment n with echo; D, update the filter tap weight coefficient, and calculate the tap weight vector of the filter at the next moment n+1 w(n+1), w(n+1)=w(n)+μ(n)U(n)(U T (n)U(n)) ‑1 E(n)-C(n); E. Let n=n+1, repeat the above-mentioned process of A, B, C, D, E until the call ends. This method can obtain faster convergence speed and low steady-state error, and the effect of echo cancellation is good.

The invention relates to the technical field of voice, and discloses an echo cancellation self-adaption method based on a PC terminal and a mobile terminal, and equipment of the method comprises sending equipment, receiving equipment and a cloud cancellation server. The sending equipment and the receiving equipment are equipment with echo elimination services, such as a mobile phone equipment, anembedded equipment and a PC equipment. The equipment already processed by the cloud elimination server does not acquire voice data, the equipment not processed by the cloud elimination server acquiresvoice data, and echo elimination parameters can be adaptively adjusted for the equipment with a feedback echo elimination effect problem in the process of using the equipment by a user. Through the design of the invention, the integration of the echo cancellation mainstream technology is realized, the support of the cloud service is increased, the real intercom environment can be simulated and restored to realize the cloud dynamic calculation of echo cancellation parameters, the echo cancellation of various equipment can be well satisfied, and the optimal echo cancellation effect is achieved.