CN105491256B - A kind of acoustic echo canceller startup stage steady step length regulating method - Google Patents
A kind of acoustic echo canceller startup stage steady step length regulating method Download PDFInfo
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Abstract
A kind of acoustic echo canceller startup stage steady step length regulating method, comprising: determine algorithm parameter；Priori filtering: current remote signaling is updated to remote signaling vector by distance n moment farthest data in removal remote signaling vector；Current echo signal is estimated using the previous state of filter, and the current echo signal estimated is filtered out from current near end signal, obtains prior uncertainty signal；Determine filter initialization step-length；Echo cancellor is carried out, according to selected step-length, carries out acoustic echo elimination using the variable step Normalized least mean squares of deficient cover half type dual end communication robust.The present invention solves VSS-NLMS-UMDT algorithm the drawbacks of the system of startup stage is easy imbalance, obtains compromise between filter convergence rate and system performance, enhances the stability and practicability of system.
Description
Technical Field
The present invention relates to an acoustic echo canceller. And more particularly to a robust step size adjustment method for the start-up phase of an acoustic echo canceller.
Background
With the continuous development of communication technology, people have higher and higher requirements on convenient communication modes, and various handheld telecommunication devices, video conferences and VoIP software telephones have wider and wider applications. In this type of communication terminal, near-end speech is transmitted from the loudspeaker to the far-end, and due to the coupling between the far-end microphone and the loudspeaker, the near-end speech is transmitted back to the local, forming an acoustic echo. Acoustic echo seriously affects the voice transmission quality, and thus an acoustic echo cancellation system is indispensable. An Acoustic Echo Canceller (AEC) is one of the best solutions to solve Acoustic Echo cancellation, and a general Acoustic Echo Canceller at least includes two parts, a Double talk detection module (DTD) and a linear Echo cancellation. Fig. 1 is a diagram of a typical acoustic echo cancellation system.
The signal received by the microphone at time n is:
d(n)＝y(n)+v(n)+w(n) 1.1
in the formula: d (n) represents the near-end signal, y (n) represents the far-end signal x (n) the echo generated after playing through the loudspeaker, v (n) represents the near-end voice signal, and w (n) represents the near-end noise signal. The remote signal x (n) is filtered by the system transfer function h to form y (n)
y(n)＝h*x 1.2
Wherein,
h＝[h0(n)h1(n),...,hN-1(n)]T
x＝[x(n)x(n-1),...,x(n-N+1)]
x is the far-end signal vector, N is the room impulse response length, and T represents the transpose of the matrix.
The objective of echo cancellation is to design an adaptive fir filter to estimate the echo path between the microphone and the loudspeakerThen, an echo estimation signal is obtained according to the estimation pathIt is eliminated from d (n) while v (n) is retained.
e (n) represents an error signal obtained after cancellation by the linear echo filter, wherein,
l is the adaptive filter length, in practice L < N. The scenario handled by an acoustic echo canceller is generally considered to be divided into three cases: far-end conditions, where only echo signals are present and near-end speech signals are not present; in the near-end situation, no echo exists, and only a near-end voice signal exists; in a double talk situation, the echo signal is present simultaneously with the near-end speech signal.
The variable Step Normalized Least mean square error algorithm (DOUBLE-TALK ROBUSTVariable Step Size Normalized Least mean square error algorithm, VSS-NLMS-UMDT) of the underdetermined DOUBLE-end call robustness is a novel and practical echo cancellation algorithm of the DOUBLE-end call robustness, compared with other Normalized Least mean square error algorithms (VSS-NLMS), the algorithm does not need a DOUBLE-end call detector (DTD), can stably work Under the conditions of the underdetermined DOUBLE-end call and the DOUBLE-end call, is insensitive to near-end signal interference, still keeps small and stable steady state imbalance, is easy to implement and control in practical application, does not need any parameter of an acoustic environment, and has strong robustness. The proposed control step size and filter update equation is
Where μ (n) is the step size of the adaptive filter, γedIs the cross-correlation estimate between e (n) and d (n), η (n) is the convergence statistic of the filter,andrespectively represent d (n),And e (n) is a constant, delta, ξ, the above parameters can be obtained from equations 1.8 and 1.9
γed(n)＝E[e(n)*d(n)]＝λγed(n-1)+(1-λ)e(n)d(n) 1.8
E {. is a mathematical expectation, λ is a very small normal, labeledAn estimate of the energy representing the sequence p (n) can be calculated by an exponential recursive formula by
Although the algorithm has obvious advantages, the algorithm still has some disadvantages. The algorithm needs to use larger step size in the system starting stageThe filter is updated to converge quickly to a stable state. If the system is in a double-end conversation state in the starting stage, the filter filters the near-end voice, which is not allowed by the system, so that the system performance is greatly reduced; if the system adopts a small control step length in the starting stageThe filter convergence speed will be slower if adjusted. Since this algorithm does not require a DTD module, the system itself cannot decide whether it is in a near-end or far-end situation, and therefore cannot select the appropriate step size to initialize the filter.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a robust step length adjustment method for an acoustic echo canceller in a start-up phase, which can not only ensure that a filter is quickly converged to a certain stable state under a double-end call condition, but also completely retain near-end speech in the start-up phase, thereby improving system performance and having stronger practicability.
The technical scheme adopted by the invention is as follows: a robust step size adjustment method for the start-up phase of an acoustic echo canceller comprises the following steps:
1) determining algorithm parameters;
2) a priori filtering, said a priori filtering comprising: removing data x (n-L) farthest from the moment n in the far-end signal vector x, and updating the current far-end signal x (n) to the far-end signal vector x; using the previous state of the filterEstimating a current echo signal, and filtering the estimated current echo signal from a current near-end signal d (n) to obtain a priori error signal epsilon (n);
3) determining the initialization step length of a filter;
4) and (3) carrying out echo cancellation, and carrying out acoustic echo cancellation by using a variable step size normalized minimum mean square error algorithm of the under-modeling double-end call robustness according to the step size selected in the step 3).
The algorithm parameters in the step 1) comprise: including speech sampling frequency fs, filter length L, filter stateFilter step size mu (n), filter step size maximum value mu max, system start time initTime, far-end signal vector x, prior error signal epsilon (n), a posteriori error signal e (n), energy expectation estimation of near-end signalEnergy expectation estimation of remote signalsEstimating an energy expectation estimate of an echo signalEnergy expectation estimation of sum error signalEstimation of cross-correlation between a priori error signal and near-end signal gammaedThe convergence statistic parameter η (n), the convergence statistic parameter expectation value exp η (n), and the convergence statistic parameter expectation threshold thres.
The step 3) of determining the initialization step size of the filter comprises the following steps:
(1) calculating a cross-correlation estimate gamma between the a priori error signal and the near-end signaledEnergy expectation estimation of near-end signalEstimating an energy expectation estimate of an echo signalEnergy expectation estimation of sum error signal
(2) Substituting the parameters in the step (1) into a calculation formula of the convergence statistical parameter η (n) to obtain a convergence statistical parameter η (n),
(3) defining expected value exp η (n) of convergence statistic parameter, and calculating by formula
expη(n)＝λ*expη(n-1)+(1-λ)*η(n) 1.11
And in the system starting time initTime, if exp η (n) is smaller than the convergence statistical parameter expected threshold thres, considering that the system is in a far-end condition currently, and updating the filter by adopting the maximum value mu max of the filter step size, otherwise, updating the step size.
The step length is updated in the step (3) by adopting the following formula:
in the formula, δ and ξ are both constants.
The method for adjusting the step length of the acoustic echo canceller in the starting stage is stable, a DTD module is not needed, and compromise is achieved between the convergence speed of a filter and the system performance. By selecting a proper step length adjusting mode at the starting stage of the acoustic echo canceller according to the expected value of the convergence statistical parameter, the filter with a large step length is selected to be rapidly converged in most of time under the far-end condition, and the filter is kept to be updated in a small step length under other conditions, so that near-end voice can be reserved, and the filter can be maintained to be rapidly updated. The method of the invention can not only ensure that the filter is quickly converged to a certain stable state under the condition of double-end conversation, but also completely reserve near-end voice in the starting stage, thereby improving the system performance and having stronger practicability. The invention solves the defect that the system of the VSS-NLMS-UMDT algorithm is easy to be out of order in the starting stage, compromises the convergence speed of the filter and the system performance, and enhances the stability and the practicability of the system.
Drawings
FIG. 1 is a typical acoustic echo cancellation system architecture;
FIG. 2a is a time domain diagram of a far-end speech signal;
FIG. 2b is a time domain diagram of a near-end signal;
FIG. 2c is a time domain diagram of a near-end speech signal;
FIG. 3a is the result of VSS-NLMS processing;
FIG. 3b is the VSS-NLMS-UMDT processing result;
FIG. 3c shows the result of the proposed method;
fig. 4 is an algorithmic flow diagram of an embodiment of the invention.
Detailed Description
The following describes a robust step adjustment method in the start-up phase of an acoustic echo canceller in detail with reference to embodiments and drawings.
The key to be solved of the robust step length adjusting method for the starting stage of the acoustic echo canceller in the invention is how to select a proper step length adjusting method for the starting stage of the acoustic echo canceller. In the starting stage, if the remote condition exists, a large-step-size updating filter is selected, so that the filter is rapidly converged; otherwise, a small step length is selected, which can not only keep the near-end voice signal, but also maintain the updating of the filter.
The invention discloses a steady step length adjusting method in the starting stage of an acoustic echo canceller, which comprises the following steps:
1) determining algorithm parameters;
the algorithm parameters comprise: including speech sampling frequency fs, filter length L, filter stateFilter step size mu (n), filter step size maximum value mu max, system start time initTime, far-end signal vector x, prior error signal epsilon (n), a posteriori error signal e (n), energy expectation estimation of near-end signalEnergy expectation estimation of remote signalsEstimating an energy expectation estimate of an echo signalAnd energy expectation of error signalEstimatingEstimation of cross-correlation between a priori error signal and near-end signal gammaedThe convergence statistic parameter η (n), the convergence statistic parameter expectation value exp η (n), and the convergence statistic parameter expectation threshold thres.
2) A priori filtering, said a priori filtering comprising: removing data x (n-L) farthest from the moment n in the far-end signal vector x, and updating the current far-end signal x (n) to the far-end signal vector x; using the previous state of the filterEstimating a current echo signal, and filtering the estimated current echo signal from a current near-end signal d (n) to obtain a priori error signal epsilon (n);
3) determining the initialization step length of a filter;
the step of determining the initialization step size of the filter comprises the following steps:
(1) calculating a cross-correlation estimate gamma between the a priori error signal and the near-end signaledEnergy expectation estimation of near-end signalEstimating an energy expectation estimate of an echo signalEnergy expectation estimation of sum error signal
(2) Substituting the parameters in the step (1) into a calculation formula of the convergence statistical parameter η (n) to obtain a convergence statistical parameter η (n),
(3) for better statistics of the convergence of the filter, the convergence statistic expected value exp η (n) is therefore defined, the calculation formula is
expη(n)＝λ*expη(n-1)+(1-λ)*η(n) 1.11
Experimental studies have found that if the filter is updated steadily at the far-end, the convergence statistic parameters expect exp η (n) to be under a certain small value thres most of the time, therefore, within the system startup time initTime, if exp η (n) is less than the convergence statistic parameters expectation threshold thres, the system is considered to be currently at the far-end, and the filter is updated with the maximum filter step size μmax, otherwise, the step size is updated according to the following formula:
wherein δ and ξ are both a constant, xLAnd (n) is a far-end signal vector.
4) And (3) carrying out echo cancellation, and carrying out acoustic echo cancellation by using a variable step size normalized minimum mean square error algorithm of the under-modeling double-end call robustness according to the step size selected in the step 3).
In the following, the scheme proposed in this patent is implemented by taking a system with a speech sampling rate of 16K and a filter length of 1000 steps as an example, and the processing steps refer to the algorithm flowchart in fig. 4.
Setting algorithm parameters: speech sampling frequency fs is 16K, filter length L is 1000, filter initial stateInitial step size mu (n) of filter is 0, constantMaximum of filter step sizeAnd filter initial time initTime 6s, far-end signal vector x 0, a priori error signal e (n) 0, a posteriori error signal e (n) 0, energy expectation estimate of near-end signal, estimated echo signal and error signalEstimation of cross-correlation between a priori error signal and near-end signal gammaedThe convergence statistic η (n) is 0, the convergence statistic expected value exp η (n) is 0, and the convergence statistic expected threshold thres is 0.05.
The algorithm comprises the following specific implementation steps:
1. reading the current far-end signal x (n) and the current near-end signal d (n), updating x (n) to the far-end signal vector xL(n) in (a);
2. calculating a priori error signal epsilon (n) through formulas 1.3 and 1.4;
3. calculating the characteristic value gamma through the formula 1.8-1.10ed,η (n) and exp η (n);
4. if the current time n < initTime L, the filter is in the start-up phase, and if the convergence statistic expected value exp η (n) is less than the convergence statistic expected threshold thres, then μ (n) is made μmax, otherwise,
1. if n ═ initTime L, the system is started and finished, and let
5. Substituting the obtained step length into the formula 1.6 to obtain the state of the filterThe a posteriori error signal e (n) is obtained after the substitution of equations 1.4 and 1.3. If the read voices are completely processed, the algorithm is terminated; otherwise, jumping back to step 1.
The a posteriori error signal e (n) is the final output of the system.
Claims (4)
1. A robust step size adjustment method for the start-up phase of an acoustic echo canceller is characterized by comprising the following steps:
1) determining algorithm parameters;
2) a priori filtering, said a priori filtering comprising: removing data x (n-L) farthest from the moment n in the far-end signal vector x, wherein L is the length of a filter, and updating the current far-end signal x (n) to the far-end signal vector x; using the previous state of the filterEstimating a current echo signal, and filtering the estimated current echo signal from a current near-end signal d (n) to obtain a priori error signal epsilon (n);
3) determining the initialization step length of a filter;
4) and (3) carrying out echo cancellation, and carrying out acoustic echo cancellation by using a variable step size normalized minimum mean square error algorithm of the under-modeling double-end call robustness according to the step size selected in the step 3).
2. The method as claimed in claim 1, wherein the algorithm parameters of step 1) include: speech sampling frequency fs, filter length L, filter stateFilter step size mu (n), filter step size maximum value mu max, system start time initTime, far-end signal vector x, prior error signal epsilon (n), a posteriori error signal e (n), energy expectation estimation of near-end signalEnergy expectation estimation of remote signalsEstimating an energy expectation estimate of an echo signalEnergy expectation estimation of sum error signalEstimation of cross-correlation between a priori error signal and near-end signal gammaed(n), convergence statistic parameter η (n), convergence statistic parameter expectation value exp η (n), convergence statistic parameter expectation threshold thres.
3. The method as claimed in claim 1, wherein said step of determining the filter initialization step size in step 3) comprises the steps of:
(1) calculating a cross-correlation estimate gamma between the a priori error signal and the near-end signaled(n), energy expectation estimation of near-end signalEstimating an energy expectation estimate of an echo signalEnergy expectation estimation of sum error signal
(2) Substituting the parameters in the step (1) into a calculation formula of the convergence statistical parameter η (n) to obtain a convergence statistical parameter η (n),
(3) defining expected value exp η (n) of convergence statistic parameter, and calculating by formula
expη(n)＝λ*expη(n-1)+(1-λ)*η(n) 1.11
In the formula, lambda is a very small normal number, and in the system starting time initTime, if exp η (n) is smaller than the convergence statistical parameter expected threshold thres, the system is considered to be in the far-end condition currently, and the filter step size maximum value mu max is adopted to update the filter, otherwise, the step size is updated.
4. The method as claimed in claim 3, wherein the step size is updated in step (3) by using the following formula:
wherein δ and ξ are both a constant, xLAnd (n) is a far-end signal vector.
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| CN111213359B (en) * | 2017-10-04 | 2021-09-10 | 主动音频有限公司 | Echo canceller and method for echo canceller |
| CN107888792B (en) | 2017-10-19 | 2019-09-17 | 浙江大华技术股份有限公司 | A kind of echo cancel method, apparatus and system |
| CN107799123B (en) * | 2017-12-14 | 2021-07-23 | 南京地平线机器人技术有限公司 | Method for controlling echo eliminator and device with echo eliminating function |
| CN111199748B (en) * | 2020-03-12 | 2022-12-27 | 紫光展锐(重庆)科技有限公司 | Echo cancellation method, device, equipment and storage medium |
| CN111355855B (en) * | 2020-03-12 | 2021-06-15 | 紫光展锐(重庆)科技有限公司 | Echo processing method, device, equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1393461A1 (en) * | 2001-05-17 | 2004-03-03 | STMicroelectronics Asia Pacific Pte Ltd. | Echo canceller and a method of cancelling echo |
| CN1956480A (en) * | 2005-10-28 | 2007-05-02 | 上海贝岭股份有限公司 | Acoustic echo removing method |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1393461A1 (en) * | 2001-05-17 | 2004-03-03 | STMicroelectronics Asia Pacific Pte Ltd. | Echo canceller and a method of cancelling echo |
| CN1956480A (en) * | 2005-10-28 | 2007-05-02 | 上海贝岭股份有限公司 | Acoustic echo removing method |
Non-Patent Citations (3)
| Title |
|---|
| Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation;Constantin Paleologu; Silviu Ciochina; Jacob Benesty;《2008 IEEE International Conference on Acoustics, Speech and Signal Processing》;20080404;245-248 * |
| Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation;Yun-Long Bi; Feng-Chang Lai; Yi-Zheng Ye;《2009 IEEE Youth Conference on Information, Computing and Telecommunication》;20090921;283-286 * |
| Novel variable step size nlms algorithms for echo cancellation;Mohammad Asif Iqbal; Steven L. Grant;《2008 IEEE International Conference on Acoustics, Speech and Signal Processing》;20080404;241-244 * |
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