CN109040499B - Adaptive echo cancellation method for resisting impact interference - Google Patents
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Abstract
An M estimation decorrelation proportional adaptive echo cancellation method resisting impact noise interference comprises the following steps: A. the method comprises the steps of collecting a far-end signal, sampling the far-end signal transmitted from a far end, and obtaining a discrete value x (n) of a far-end input signal at the current time n, wherein a filter input signal vector is x (n) ═ x (n), x (n-1),.., x (n-L +1)]T(ii) a B. Echo signal estimation, namely, passing an input signal vector x (n) at the time n through an adaptive filter, and outputting an output value y (n), namely, an estimated value of the echo signal; C. performing decorrelation operation on an input signal, calculating a correlation coefficient a (n), and obtaining an update vector z (n) of a tap weight vector w (n) of the adaptive filter; D. echo cancellation, which is to use a near-end microphone to sample and obtain a near-end signal d (n) with echo at the current time n and subtract an estimated value y (n) of the echo signal; E. updating the tap weight coefficient of the filter, calculating the updated evaluation function psi (e (n)) and the updated step size matrix G (n) of the tap weight vector w (n) of the current moment n of the filter; F. and (5) repeating the processes of A, B, C, D and E until the call is ended by making n equal to n + 1.
Description
Technical Field
The invention belongs to the technical field of echo cancellation of communication systems.
Technical Field
Adaptive signal processing techniques have found wide application in the field of communications. Signals in a communication system are mainly voice signals, however, when a call is made, echo can seriously affect the quality of the voice call. Echo refers to the reflection of sound or signals back to the signal source through time delays or distortions. Such a phenomenon is widely present in communication systems such as voice communication, data communication, satellite communication, hands-free telephone, and telephone conference systems. For example, when a call is made, because the speaker and the microphone are located in the same space, the local near-end microphone receives the far-end speech from the local speaker and transmits the far-end speech back, which causes the far-end speaker to hear his own voice. It is necessary to suppress echo signals, remove the influence thereof, and improve the voice call quality by taking effective measures. The adaptive echo cancellation technology has low cost, high convergence rate and small echo residual, is widely concerned and is considered as the most promising echo cancellation technology. The adaptive echo cancellation technique achieves the purpose of echo cancellation by estimating the echo signal and subtracting the estimated value of the echo from the near-end signal.
The self-adaptive echo cancellation system is a sparse system, the length of an impulse response system can reach hundreds of symbols, but only a few effective factors are nonzero coefficients, so that the convergence speed is low; when the input signal contains impact noise, the convergence speed is slower, the steady-state error is larger, and the echo cancellation performance is seriously reduced. Document 1, "research on proportional adaptive algorithm for sparse system identification" (dunghill, southwest university of transportation [ D ], 2016 ]) combines the idea of decorrelation and the idea of proportionality, proposes a proportional adaptive algorithm based on decorrelation, which is applied to a sparse system, accelerates the convergence rate of an adaptive filtering algorithm, and reduces a steady-state error. In the product influence factors considered during updating of the tap weight vector of the algorithm, the influence factors related to the residual error are directly residual error signals at the current moment, and when impact interference exists, the residual error signals are huge, so that the tap weight vector is updated in a wrong and huge manner, the convergence speed is reduced, the steady-state error is increased, and good effect can not be obtained when the impact interference is resisted.
Disclosure of Invention
The invention aims to provide an anti-impact interference self-adaptive echo cancellation method which has strong anti-impact interference capability, still has higher convergence speed and low steady-state error when an impact interference signal exists and has good echo cancellation effect.
The technical scheme adopted by the invention for realizing the aim is that the adaptive echo cancellation method for resisting the impact interference comprises the following steps:
A. remote signal acquisition
For signals transmitted from far endLine sampling is carried out, a discrete value x (n) of a far-end input signal at the current time n is obtained, and input signals x (n), x (n-1) from the current time n to n-L +1 time are combined into an adaptive filter input vector x (n) at the current time n; x (n) ═ x (n), x (n-1),.., x (n-L +1)]TWhere T represents the transpose operation, and L512 represents the number of filter taps;
B. echo signal estimation
The input signal vector x (n) of the current time n is passed through the adaptive filter to obtain the output value of the adaptive filter, i.e. the estimated value y (n) of echo signal,
y(n)＝xT(n)w(n)
where w (n) is the weight vector of the adaptive filter taps at the current time instant n, w (n) ═ w1(n),w2(n),...,wL-1(n)]TThe initial value of w (n) is a zero vector;
C. decorrelation of input signals
Calculating a correlation coefficient a (n) between an adaptive filter input vector x (n) at a current time n and an adaptive filter input vector x (n-1) at a previous time:
obtaining an update vector z (n) of an adaptive filter input signal vector x (n), wherein z (n) is x (n) -a (n) x (n-1) by a decorrelation operation;
D. echo cancellation
Sampling a near-end microphone to obtain a near-end signal d (n) with echo at the current time n, subtracting an estimated value y (n) of the echo signal from the near-end microphone to obtain an error signal e (n) at the current time n, wherein e (n) is d (n) -y (n), and sending the error signal e (n) at the current time n back to the far end;
E. filter tap weight vector update
E1, calculating cost function
According to the current time N to the time N-Nw+1 residual signals e (N), e (N-1), …, e (N-N)w+1) to obtain the current time N to the time N-Nw+1 intervalIs the square value e of the residual signal2(n),e2(n-1),…,e2(n-Nw+1) to obtain the square sequence A of the residual signal in the estimation window of the current time ne(n)，
Ae(n)＝[e2(n),e2(n-1),…,e2(n-Nw+1)]
Wherein N iswThe length of the estimation window is in a range of 5-15;
then, the weighted normalized residual error of the current time n is calculated by the following formula
Wherein, λ is the weight of the previous time N-1, the value range is 0.800-0.999, C is a standardized parameter, and C is 1.483(1+ 5/(N)w-1)), med (-) represents an operation taking an intermediate value;
weighted normalized residual from current time n
Obtaining M estimated larger threshold parameter delta of current time n1(n)，
Obtaining M estimated larger threshold parameter delta of current time n2(n)，
Obtaining M estimated larger threshold parameter delta of current time n3(n)，
The evaluation function ψ (e (n)) updated by the tap weight vector w (n) at the current time n of the filter is calculated by the following equation:
wherein sgn (·) is a sign function;
e2, calculating a proportional matrix
Calculating an update step size matrix G (n) of a filter tap weight vector w (n), G (n) diag [ g [ () ]1(n),g2(n),...gl(n)...,gL(n)]Wherein g isl(n) represents the ith proportional control factor, which can be calculated by:
wherein | · | purple1Represents a 1-norm, diag represents a diagonal matrix, beta is a proportional parameter, and beta is ∈ [ -1,1]Representing a regularization parameter, wherein the value range is 0.001-0.01;
e3, updating of filter tap weight vector
Updating a filter tap weight vector w (n +1) of the next time n +1 by using a decorrelation proportional normalization adaptive filtering method based on M estimation:
mu represents the step length of the filter, the value range is 0-2, and the mu is a very small normal number, so that the condition that the denominator in the formula is 0 and the value is 0.001-0.01 is avoided;
F. and (5) repeating the processes of A, B, C, D and E until the call is ended when n is equal to n + 1.
Compared with the prior art, the invention has the beneficial effects that:
in the product influence factors considered in updating the tap weight vector of the present invention, the influence factor related to the residual is not the residual signal e (n) at the current time but the evaluation function ψ (e (n)) based on the weighted normalized residual.
First, the weighted normalized residual at the current time
Is the weighted normalized residual of the previous time instant and the time window of the current time instant (N including the current time instant)wTime instant) of the residual signal squared e2(n) a weighted average of the median; the normalized residual formed by the median of the square of the time window residual at the current moment is already reduced once, and the weighted normalized residual obtained by weighted averaging with the normalized residual at the previous moment is reduced again.
Next, the merit function ψ (e (n)) based on the weighted normalized residual: when the residual error is less than a large threshold value delta1(n), determining that no impact interference signal exists at the moment, and taking residual signals e (n) by the evaluation function psi (e (n)), wherein the information contained in the residual signals is fully utilized, the convergence rate is high, and the steady-state error is low; when the residual error is greater than a large threshold value delta1(n) is less than a larger threshold value Delta2When (n) is detected, weak impact interference signal is determined, and the evaluation function psi (e (n)) takes on value delta1(n) minus Δ1(n), thereby weakly reducing the weak impact interference signal and effectively utilizing the information in the residual signal; when the residual error is larger than a larger threshold value delta2(n) less than a maximum threshold value Δ3(n), a strong impulse interference signal is determined, and the evaluation function psi (e (n)) is obtained
That is, the evaluation function psi (e (n)) and e (n) are in negative correlation and have a value range of delta1(n) to 0, thereby reducing the stronger impact interference signal; when the residual error is greater than the maximum threshold value delta3When (n) is detected, the super-strong impact interference signal is determined, and the evaluation function psi (e (n)) takes 0 to cut off all the super-strong impact signals.
In a word, when no impact interference signal exists, the method directly takes the error signal as the product influence factor when the weight vector is updated, fully utilizes the information in the residual signal, and has low steady-state error and high convergence speed as in the prior art; when an impact interference signal exists, the stronger impact signal is reduced to a greater extent by dividing the impact interference signal into three threshold sections until the impact signal is completely reduced, so that the impact resistance is strong. When the impact interference signal exists, the method has the advantages of higher convergence speed and lower steady-state error, and the echo cancellation effect is good.
Meanwhile, the invention obtains the correlation coefficient a (n) by using the idea of understanding the correlation, and subtracts the correlation component a (n) x (n-1) from the input vector x (n) of the current moment n to obtain the update vector z (n) of the input vector of the adaptive filter, so that the adaptive filter has higher convergence speed and low steady-state error, and the echo cancellation effect is better.
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a graph of the proximal signal in the experiment.
FIG. 2 is a graph of the far end signal in the experiment.
Fig. 3 is a normalized steady-state detuning curve of document 1 and the method of the present invention at a step size μ of 0.9.
Fig. 4 is a normalized steady-state detuning curve of document 1 and the method of the present invention at a step size μ equal to 0.4.
Detailed Description
Examples
A specific embodiment of the present invention is an adaptive echo cancellation method for resisting impact interference, which includes the following steps:
A. remote signal acquisition
Sampling a signal transmitted from a far end to obtain a discrete value x (n) of a far end input signal at the current time n, and forming an adaptive filter input vector x (n) at the current time n by input signals x (n), x (n-1) from the current time n to n-L + 1; x (n) ═ x (n), x (n-1),.., x (n-L +1)]TWhere T represents the transpose operation, and L512 represents the number of filter taps;
B. echo signal estimation
The input signal vector x (n) of the current time n is passed through the adaptive filter to obtain the output value of the adaptive filter, i.e. the estimated value y (n) of echo signal,
y(n)＝xT(n)w(n)
where w (n) is the weight vector of the adaptive filter taps at the current time instant n, w (n) ═ w1(n),w2(n),...,wL-1(n)]TThe initial value of w (n) is a zero vector;
C. decorrelation of input signals
Calculating a correlation coefficient a (n) between an adaptive filter input vector x (n) at a current time n and an adaptive filter input vector x (n-1) at a previous time:
obtaining an update vector z (n) of an adaptive filter input signal vector x (n), wherein z (n) is x (n) -a (n) x (n-1) by a decorrelation operation;
D. echo cancellation
Sampling a near-end microphone to obtain a near-end signal d (n) with echo at the current time n, subtracting an estimated value y (n) of the echo signal from the near-end microphone to obtain an error signal e (n) at the current time n, wherein e (n) is d (n) -y (n), and sending the error signal e (n) at the current time n back to the far end;
E. filter tap weight vector update
E1, calculating cost function
According to the current time N to the time N-Nw+1 residual signals e (N), e (N-1), …, e (N-N)w+1) to obtain the current time N to the time N-NwThe square e of the residual signal between +12(n),e2(n-1),…,e2(n-Nw+1) to obtain the square sequence A of the residual signal in the estimation window of the current time ne(n)，
Ae(n)＝[e2(n),e2(n-1),…,e2(n-Nw+1)]
Wherein N iswThe length of the estimation window is in a range of 5-15;
then, the weighted normalized residual error of the current time n is calculated by the following formula
Wherein, λ is the weight of the previous time N-1, the value range is 0.800-0.999, C is a standardized parameter, and C is 1.483(1+ 5/(N)w-1)), med (-) represents an operation taking an intermediate value;
weighted normalized residual from current time n
Obtaining M estimated larger threshold parameter delta of current time n1(n)，
Obtaining M estimated larger threshold parameter delta of current time n2(n)，
Obtaining M estimated larger threshold parameter delta of current time n3(n)，
The evaluation function ψ (e (n)) updated by the tap weight vector w (n) at the current time n of the filter is calculated by the following equation:
wherein sgn (·) is a sign function;
e2, calculating a proportional matrix
Calculating an update step size matrix G (n) of a filter tap weight vector w (n), G (n) diag [ g [ () ]1(n),g2(n),...gl(n)...,gL(n)]Wherein g isl(n) represents the ith proportional control factor, which can be calculated by:
wherein | · | purple1Represents a 1-norm, diag represents a diagonal matrix, beta is a proportional parameter, and beta is ∈ [ -1,1]Representing a regularization parameter, wherein the value range is 0.001-0.01;
e3, updating of filter tap weight vector
Updating a filter tap weight vector w (n +1) of the next time n +1 by using a decorrelation proportional normalization adaptive filtering method based on M estimation:
mu represents the step length of the filter, the value range is 0-2, and the mu is a very small normal number, so that the condition that the denominator in the formula is 0 and the value is 0.001-0.01 is avoided;
F. and (5) repeating the processes of A, B, C, D and E until the call is ended when n is equal to n + 1.
Simulation experiment
To verify the effectiveness of the present invention, simulation experiments were performed and the method of document 1 was compared to the method of the present invention.
The far-end signal x (n) of the simulation experiment is a colored signal, see fig. 2. It is white gaussian noise which is obtained by a first-order autoregressive process T (z) 1/(1-0.9 z)-1) The generated sampling frequency is 8000Hz, the number of sampling points is 40000, and the current time value and the last time value of the colored signal are related.
The echo channel impulse response is obtained in a quiet closed room with the width of 3.75m, the height of 2.5m, the length of 6.25m, the temperature of 20 ℃ and the humidity of 50%, and the impulse response length, namely the number L of filter taps is 512.
The experimental background noise was: impulse noise (impulse noise s (n) is generated by Bernoulli Gaussian signal simulation) is added to white Gaussian noise v (n) with a signal-to-noise ratio of 30 dB. The background noise plus the far-end signal x (n) constitutes the near-end signal, see fig. 1.
The performance of two different echo cancellation methods is measured by using normalized steady state imbalance (NMSD) in the simulation experiment, and the formula is as follows:
wherein w0A weight vector representing an unknown echo path.
The above-mentioned far-end signal and the corresponding near-end signal are used for echo cancellation by the method of the present invention and the method of the reference. The optimal parameter values for both methods are shown in table 1.
Table 1 optimum parameter approximation values for two methods of experiment
The simulation experiment obtains a simulation result by independently operating for 100 times. Fig. 3 and 4 are normalized steady-state imbalance curves for the method of reference 1 and the method of the present invention at asynchronous lengths.
As can be seen from fig. 3, in the sparse system, the input signal is a correlated signal and the impulse noise is added, and under the condition that the convergence rates are approximately the same, the method of the document 1 is approximately stabilized at-25 dB, the method of the present invention is approximately stabilized at-29 dB, and the steady-state error of the method of the present invention is 4dB lower than that of the method of the document 1. As can be seen from fig. 4, when the steady-state errors are substantially the same, the convergence rate of document 1 is about 15000 points, and the convergence rate of the method of the present invention is about 5000 points, so that the convergence rate of the present invention is faster. In fig. 3 and 4, the curve of the method of the present invention is smoother after convergence, which also indicates that the method has better resistance to impulse noise and better echo cancellation effect.
Claims (1)
1. An adaptive echo cancellation method for resisting shock interference comprises the following steps:
A. remote signal acquisition
Sampling a signal transmitted from a far end to obtain a discrete value x (n) of a far end input signal at the current time n, and forming an adaptive filter input vector x (n) at the current time n by input signals x (n), x (n-1) from the current time n to n-L + 1; x (n) ═ x (n), x (n-1),.., x (n-L +1)]TWhere T represents the transpose operation, and L512 represents the number of filter taps;
B. echo signal estimation
The input signal vector x (n) of the current time n is passed through the adaptive filter to obtain the output value of the adaptive filter, i.e. the estimated value y (n) of echo signal,
y(n)＝xT(n)w(n)
where w (n) is the weight vector of the adaptive filter taps at the current time instant n, w (n) ═ w1(n),w2(n),...,wL-1(n)]TThe initial value of w (n) is a zero vector;
C. decorrelation of input signals
Calculating a correlation coefficient a (n) between an adaptive filter input vector x (n) at a current time n and an adaptive filter input vector x (n-1) at a previous time:
obtaining an update vector z (n) of an adaptive filter input signal vector x (n), wherein z (n) is x (n) -a (n) x (n-1) by a decorrelation operation;
D. echo cancellation
Sampling a near-end microphone to obtain a near-end signal d (n) with echo at the current time n, subtracting an estimated value y (n) of the echo signal from the near-end microphone to obtain an error signal e (n) at the current time n, wherein e (n) is d (n) -y (n), and sending the error signal e (n) at the current time n back to the far end;
E. filter tap weight vector update
E1, calculating cost function
According to the current time N to the time N-Nw+1 residual signals e (N), e (N-1), …, e (N-N)w+1) to obtain the current time N to the time N-NwThe square e of the residual signal between +12(n),e2(n-1),…,e2(n-Nw+1) to obtain the square sequence A of the residual signal in the estimation window of the current time ne(n)，
Ae(n)＝[e2(n),e2(n-1),…,e2(n-Nw+1)]
Wherein N iswThe length of the estimation window is in a range of 5-15;
then, the weighted normalized residual error of the current time n is calculated by the following formula
Wherein, λ is the weight of the previous time N-1, the value range is 0.800-0.999, C is a standardized parameter, and C is 1.483(1+ 5/(N)w-1)), med (-) represents an operation taking an intermediate value;
weighted normalized residual from current time n
Obtaining M estimated larger threshold parameter delta of current time n1(n)，
Obtaining M estimated larger threshold parameter delta of current time n2(n)，
Obtaining M estimated larger threshold parameter delta of current time n3(n)，
The evaluation function ψ (e (n)) updated by the tap weight vector w (n) at the current time n of the filter is calculated by the following equation:
wherein sgn (·) is a sign function;
e2, calculating a proportional matrix
Calculating an update step size matrix G (n) of a filter tap weight vector w (n), G (n) diag [ g [ () ]1(n),g2(n),...gl(n)...,gL(n)]Wherein g isl(n) represents the l-th proportional control factor, calculated by the following formula:
wherein | · | purple1Represents a 1-norm, diag represents a diagonal matrix, beta is a proportional parameter, and beta is ∈ [ -1,1]Representing a regularization parameter, wherein the value range is 0.001-0.01;
e3, updating of filter tap weight vector
Updating a filter tap weight vector w (n +1) of the next time n +1 by using a decorrelation proportional normalization adaptive filtering method based on M estimation:
mu represents the step length of the filter, the value range is 0-2, and the mu is a very small normal number, so that the condition that the denominator in the formula is 0 and the value is 0.001-0.01 is avoided;
F. and (5) repeating the processes of A, B, C, D and E until the call is ended when n is equal to n + 1.
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| CN109785853B (en) * | 2019-03-11 | 2020-06-16 | 出门问问信息科技有限公司 | Echo cancellation method, device, system and storage medium |
| CN110246513B (en) * | 2019-07-08 | 2021-08-24 | 维沃移动通信有限公司 | Voice signal processing method and mobile terminal |
| CN110599997B (en) * | 2019-09-25 | 2022-04-12 | 西南交通大学 | Impact noise active control method with strong robustness |
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| CN112397080B (en) * | 2020-10-30 | 2023-02-28 | 浙江大华技术股份有限公司 | Echo cancellation method and apparatus, voice device, and computer-readable storage medium |
| CN113345457B (en) * | 2021-06-01 | 2022-06-17 | 广西大学 | Acoustic echo cancellation adaptive filter based on Bayes theory and filtering method |
| CN113362844B (en) * | 2021-07-26 | 2022-05-10 | 西南交通大学 | Low-complexity decorrelation self-adaptive acoustic echo cancellation method and device |
| CN114400016A (en) * | 2022-01-21 | 2022-04-26 | 西南科技大学 | Echo cancellation method based on adaptive decorrelation and variable-step proportional M estimation |
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