CN118138690B - Robust echo cancellation method and system for telephone communications - Google Patents
Robust echo cancellation method and system for telephone communications Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M9/00—Arrangements for interconnection not involving centralised switching
- H04M9/08—Two-way loud-speaking telephone systems with means for conditioning the signal, e.g. for suppressing echoes for one or both directions of traffic
- H04M9/082—Two-way loud-speaking telephone systems with means for conditioning the signal, e.g. for suppressing echoes for one or both directions of traffic using echo cancellers
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- G—PHYSICS
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- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
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- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
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- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech
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Abstract
The application relates to the field of telephone communication, in particular to a robust echo cancellation method and a system for telephone communication, wherein the method comprises the following steps: respectively constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and a noise-containing voice signal sampling value; performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal; generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal; generating a mean square deviation estimation value according to the error signal variance estimation value and the self-adaptive weight vector; constructing an augmentation weight vector according to the self-adaptive weight vector; generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value; generating a step value according to the optimal step value; and updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step size value. A low steady state error is achieved while converging quickly.
Description
Technical Field
The invention relates to the field of telephone communication, in particular to a robust echo cancellation method and a system for telephone communication.
Background
System identification is an important branch of adaptive signal processing, and many problems such as traditional adaptive channel equalization, adaptive noise cancellation, adaptive echo cancellation, active noise control and the like can be reduced to system identification problems. Traditional least mean square (abbreviated LMS) and normalized least mean square (abbreviated NLMS) echo cancellation methods are easy to implement, but in some special environments, performance can drop dramatically. For example, in some cases, the output signal of an unknown system may be contaminated with impulse noise. In order to enhance the anti-impulse interference capability of the echo cancellation method, scholars propose the echo cancellation method such as maximum correlation entropy, minimum error entropy and the like by utilizing the concept of entropy in information theory learning.
In addition, the conventional echo cancellation method assumes that the input signal is noiseless, but in practical application, noise is generally mixed in when the input signal is acquired. Therefore, the above-described echo cancellation method cannot provide an unbiased estimate for the echo channel. The overall least squares method can generally be used to solve this problem. At present, scholars propose some anti-pulse echo cancellation methods suitable for the input noise-containing scene, such as a maximum overall correlation entropy method, a maximum overall generalized correlation entropy method and the like. However, these methods have the problem of large steady-state error, and there is a contradiction between the convergence speed and the steady-state error, so that the low steady-state error cannot be obtained while the convergence is fast.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
Therefore, the invention aims to solve the technical problems that the steady state error is larger in the prior art, the convergence speed and the steady state error are contradictory, and the low steady state error can not be obtained while the convergence is fast.
To solve the above technical problem, a first aspect of the present invention provides a robust echo cancellation method for telephone communication, the method comprising:
Acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller;
constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
Performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal;
generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
Generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector;
constructing an augmentation weight vector according to the self-adaptive weight vector;
generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value;
Generating a step value according to the optimal step value;
And updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step value.
In one embodiment of the invention, the adaptive weight vector is expressed as:
；
Wherein, For M self-adaptive weights, the subscript n represents time, and the superscript T represents transposition operation;
The noisy input signal vector is expressed as:
；
Wherein, For M sample values of the noisy speech signal, the subscript n represents the time and the superscript T represents the transpose operation.
In one embodiment of the invention, the output signal is represented as:
；
Wherein, In order to make a vector of noisy input signals,For the adaptive weight vector, the superscript T denotes a transpose operation.
In one embodiment of the invention, the error signal is expressed as:
；
Wherein, And represents the noise-containing echo signal at the time n, namely the noise-containing expected signal.
In one embodiment of the invention, the error signal variance estimate is expressed as:
；
Wherein, The value range is 0.99 to 0.999 for smoothing factors,An error signal variance estimate for time n-1,Is median filtering, L is the length of the median filtering.
In one embodiment of the invention, the augmented weight vector is expressed as:
；
Wherein, Is the ratio of the noise to the noise,Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the input noise variance.
In one embodiment of the invention, the first parameter value is expressed as:
；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the second parameter value is expressed as:
；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the third parameter value is expressed as:
；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the fourth parameter value is expressed as:
；
Wherein, Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For a 2-norm.
In one embodiment of the invention, the optimal step value is expressed as:
；
Wherein, 、、AndAll are relevant parameter values at the moment n;
the step value is expressed as:
；
Wherein, Is a step smoothing factor with a value range of 0.99 to 0.999,Is the initial set upper limit of the step size,Is the step size at time n-1.
In one embodiment of the present invention, the formula for updating the adaptive weight vector is:
；
Wherein, In order to make a vector of noisy input signals,In order to adapt the weight vector to the model,As an error signal, the signal is a signal,In order to augment the weight vector,In order to obtain the 2-norm,Is a kernel width parameter.
A second aspect of the present invention provides a robust echo cancellation system for telephony, the system comprising: the device comprises a vector construction module, a first calculation module, a second calculation module and an updating module;
The vector construction module is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller; constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
The first computing module is configured to: performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal; generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
The second computing module is configured to: generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector; constructing an augmentation weight vector according to the self-adaptive weight vector; generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value; generating a step value according to the optimal step value; the related parameter values include a first parameter value, a second parameter value, a third parameter value, and a fourth parameter value;
the update module is configured to: and updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step value.
Compared with the prior art, the technical scheme of the invention has the following advantages:
According to the robust echo cancellation method and system for telephone communication, the purpose of realizing lower steady-state error while fast convergence is achieved by adding the calculated step value and updating the self-adaptive weight vector according to the step value.
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In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings.
FIG. 1 is a flow chart of a robust echo cancellation method for telephony communications provided by the present invention;
Fig. 2 is a waveform diagram of an echo channel in a robust echo cancellation method and system for telephone communication according to the present invention;
FIG. 3 is a waveform diagram of a voice signal in a robust echo cancellation method and system for telephony according to the present invention;
FIG. 4 is a graph showing a comparison of normalized mean square deviation curves when the probability of occurrence of impulse noise is 0.01 in a robust echo cancellation method and system for telephone communication provided by the present invention;
FIG. 5 is a graph showing a comparison of normalized mean square deviation curves when the probability of occurrence of impulse noise is 0.1 in a robust echo cancellation method and system for telephone communication provided by the present invention;
fig. 6 is a block diagram of a robust echo cancellation system for telephony according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
In addition, the described embodiments are only some, but not all, embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art based on embodiments of the application without making any inventive effort, fall within the scope of the application.
Referring to fig. 1, the present invention provides a robust echo cancellation method for telephony communications, the method comprising:
S100, acquiring an adaptive weight of an echo canceller and a sampling value of a noise-containing voice signal;
In step S100, M adaptive weights of the echo canceller at the current n time are obtained Acquiring noise-containing voice signal sampling values of the current n moments and M-1 moments before the current n moments。
S200, constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
in step S200, the adaptive weight vector is expressed as:
（1）；
Wherein, For M self-adaptive weights, the subscript n represents time, and the superscript T represents transposition operation;
The noisy input signal vector is expressed as:
（2）；
Wherein, For M sample values of the noisy speech signal, the subscript n represents the time and the superscript T represents the transpose operation.
In the practical application scene, M self-adaptive weights of the echo canceller at the n moment are obtainedThe noise-containing voice signal sampling values of the n moments and the continuous M-1 moments before the n moments are combined to form an adaptive weight vectorAnd combining to form a noisy input signal vector.
S300, carrying out inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal;
In step S300, the output signal is expressed as:
（3）；
Wherein, In order to make a vector of noisy input signals,For the adaptive weight vector, the superscript T denotes a transpose operation.
In an actual application scene, carrying out inner product on the noisy input signal vector and the self-adaptive weight vector to obtain an output signal at the moment n.
S400, generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
in step S400, the error signal is expressed as:
（4）；
Wherein, And represents the noise-containing echo signal at the time n, namely the noise-containing expected signal.
The error signal variance estimate is expressed as:
（5）；
Wherein, The value range is 0.99 to 0.999 for smoothing factors,An error signal variance estimate for time n-1,Is median filtering, L is the length of the median filtering.
In an actual application scenario, an error signal at time n is calculated according to equation (4). Error signal based on n time and past L-1 timeAnd equation (5) calculating an error signal variance estimate at time n,Is a smoothing factor used to ensure the accuracy of the estimation.
S500, generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector;
In step S500, a lower limit of the mean square deviation at time n is calculated according to equation (6) 。
（6）；
Wherein, Is the mean square deviation estimated value at the time of n-1; Is the step size at time n-1; Is the first parameter value of the n-1 time related parameter values, Is the second one of the n-1 time related parameter values,Is the third parameter value among the n-1 time-related parameter values,The fourth parameter value is the parameter value related to the n-1 time, and then the mean square deviation estimated value of the n time is calculated according to the formula (7).
（7）；
Wherein, Is the variance of the input signal.
S600, constructing an augmentation weight vector according to the self-adaptive weight vector;
In step S600, the augmentation weight vector is expressed as:
（8）；
Wherein, Is the ratio of the noise to the noise,Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the input noise variance.
In an actual application scenario, an augmentation weight vector is calculated according to equation (8).
S700, generating a relevant parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the relevant parameter value; the related parameter values include a first parameter value, a second parameter value, a third parameter value, and a fourth parameter value;
in step S700, the first parameter value is expressed as:
（9）；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the second parameter value is expressed as:
（10）；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the third parameter value is expressed as:
（11）；
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For 2 norms;
the fourth parameter value is expressed as:
（12）；
Wherein, Is an estimate of the mean square deviation at time n,Is the variance of the input signal and,Is a parameter of the core width and,For a 2-norm.
The optimal step value is expressed as:
（13）；
Wherein, 、、AndAll are relevant parameter values at the moment n;
In an actual application scene, generating a relevant parameter value according to the augmented weight vector and the error signal variance estimation value, wherein the relevant parameter value comprises a first parameter value, a second parameter value, a third parameter value and a fourth parameter value, calculating the first parameter value through a formula (9), calculating the second parameter value through a formula (10), calculating the third parameter value through a formula (11), calculating the fourth parameter value through a formula (12), and calculating the optimal step length at the moment n according to the obtained relevant parameter value and a formula (13) 。
S800, generating a step value according to the optimal step value;
In step S800, the step value is expressed as:
（14）；
Wherein, Is a step smoothing factor with a value range of 0.99 to 0.999,Is the initial set upper limit of the step size,Is the step size at time n-1.
In the practical application scene, calculating the step length of n time according to the formula (14)。
S900, updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step value.
In step S800, the formula for updating the adaptive weight vector is:
（15）；
Wherein, In order to make a vector of noisy input signals,In order to adapt the weight vector to the model,As an error signal, the signal is a signal,In order to augment the weight vector,In order to obtain the 2-norm,Is a kernel width parameter.
In an actual application scene, the adaptive weight vector is updated according to the iteration type (15). In this embodiment, a robust echo cancellation method for telephone communication (abbreviated as VSS-MTLDM) adopts a computer experimental method to verify the performance of the VSS-MTLDM echo cancellation method applied to an echo cancellation task in telephone communication and is similar to the existing method: the experimental results of the maximum overall correlation entropy echo cancellation method MTC[Maximum total correntropy adaptive filtering against heavy-tailed noises, Signal Processing, 2017, 141: 84–95] and the maximum overall generalized correlation entropy echo cancellation method MTGC[Maximum total generalized correntropy adaptive filtering for parameter estimation, Signal Processing, 2023, 203: 108787] are compared. In addition, in the present embodiment, the step size of the robust echo cancellation method for telephone communication is also set to a fixed value (abbreviated as MTLDM) for comparison. All experiments were performed in MATLAB2018a on a 2.9 GHz intel borui 5 personal computer.
Experiments were performed in an echo cancellation environment in telephone communications using the echo channel shown with reference to fig. 2 as an unknown system and using the speech signal shown with reference to fig. 3 as an input signal for the echo channel. Gaussian white signal with zero mean of input noise, variance is set as. The measurement noise being generated by a Gaussian mixture model, i.e.Wherein, the method comprises the steps of, wherein,Is the variance ofIs a zero-mean gaussian white noise of (c),Is impulse noise generated by the Bernoulli Gaussian process, i.e，Is a Bernoulli process, and the probability of taking 1 is set to 0.01 and 0.1 respectively,Is a variance ofIs a zero-mean gaussian white noise of (c). Using Normalized Mean Square Deviation (NMSD) as a measure of performance, i.eIn units ofWhereinThe log is represented. The NMSD curves simulated in the figure are all obtained by averaging 100 independent iterations. The experimental results of echo cancellation referring to fig. 4 and 5 show that the VSS-MTLDM echo cancellation method according to the embodiment of the present application can effectively cancel echo in a pulse environment in which noise is input.
In a second aspect, and with reference to fig. 6, the present application provides a robust echo cancellation system for telephony, the system comprising: a vector construction module 100, a first calculation module 200, a second calculation module 300, an update module 400;
The vector construction module 100 is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller; constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
The first computing module 200 is configured to: performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal; generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
The second computing module 300 is configured to: generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector; constructing an augmentation weight vector according to the self-adaptive weight vector; generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value; generating a step value according to the optimal step value; the related parameter values include a first parameter value, a second parameter value, a third parameter value, and a fourth parameter value;
the update module 400 is configured to: and updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step value.
The effects of the above system when the above method is applied may be referred to the description in the foregoing method embodiment, and will not be repeated here.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (2)
1. A robust echo cancellation method for telephony communications, the method comprising:
Acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller;
constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
Performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal;
generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
Generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector;
constructing an augmentation weight vector according to the self-adaptive weight vector;
Generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value; the related parameter values include a first parameter value, a second parameter value, a third parameter value, and a fourth parameter value;
Generating a step value according to the optimal step value;
updating the adaptive weight vector according to the adaptive weight vector, the augmented weight vector, the error signal and the step size value;
the adaptive weight vector is expressed as:
wn＝[wn,0,wn,1,…,wn,M-1]T；
Wherein { w n,0,wn,1,…,wn,M-1 } is M self-adaptive weights, the subscript n represents the moment, and the superscript T represents the transposition operation;
The noisy input signal vector is expressed as:
Wherein, For M sampling values of the noise-containing voice signal, the subscript n represents time, and the superscript T represents transposition operation;
the output signal is expressed as:
Wherein, For a noisy input signal vector, w n is an adaptive weight vector, and the superscript T represents a transpose operation;
the error signal is expressed as:
Wherein, Representing a noise-containing echo signal at the time n, namely a noise-containing expected signal;
The error signal variance estimate is expressed as:
wherein alpha is a smoothing factor, the value range is 0.99 to 0.999, For the error signal variance estimation value at time n-1, mean (·) is median filtering, and L is the length of median filtering;
The augmented weight vector is expressed as:
Wherein, Is the ratio of the noise to the noise,Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the input noise variance;
the first parameter value is expressed as:
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal, ε is the kernel width parameter, is the 2-norm;
the second parameter value is expressed as:
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal, ε is the kernel width parameter, is the 2-norm;
the third parameter value is expressed as:
Wherein, Is the variance of the measurement noise in the desired signal without impulse noise interference,Is the variance of the input noise and,Is an estimate of the mean square deviation at time n,Is the variance of the input signal, ε is the kernel width parameter, is the 2-norm;
the fourth parameter value is expressed as:
Wherein, Is an estimate of the mean square deviation at time n,Is the variance of the input signal, ε is the kernel width parameter, is the 2-norm;
The optimal step value is expressed as:
wherein a n、bn、cn and m n are both related parameter values at the time of n;
the step value is expressed as:
Wherein, beta is a step length smoothing factor, the value range is 0.99 to 0.999, mu max is the initial set step length upper limit, mu n-1 is the step length at the time of n-1;
the formula for updating the self-adaptive weight vector is as follows:
Wherein, For noisy input signal vectors, w n is an adaptive weight vector, e n is an error signal,To augment the weight vector, to 2 norms, ε is the kernel width parameter.
2. A robust echo cancellation system for telephony communications, for performing the robust echo cancellation method for telephony communications of claim 1, the system comprising: the device comprises a vector construction module, a first calculation module, a second calculation module and an updating module;
The vector construction module is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller; constructing an adaptive weight vector and a noise-containing input signal vector according to the adaptive weight and the noise-containing voice signal sampling value respectively;
The first computing module is configured to: performing inner product on the self-adaptive weight vector and the noise-containing input signal vector to generate an output signal; generating an error signal according to the output signal, and generating an error signal variance estimation value according to the error signal;
The second computing module is configured to: generating a mean square deviation estimated value according to the error signal variance estimated value and the self-adaptive weight vector; constructing an augmentation weight vector according to the self-adaptive weight vector; generating a related parameter value according to the augmentation weight vector and the error signal variance estimation value, and generating an optimal step value according to the related parameter value; generating a step value according to the optimal step value; the related parameter values include a first parameter value, a second parameter value, a third parameter value, and a fourth parameter value;
the update module is configured to: and updating the self-adaptive weight vector according to the self-adaptive weight vector, the augmentation weight vector, the error signal and the step value.
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