CN117896468A - Deviation compensation echo cancellation method and system for telephone communication - Google Patents
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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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- 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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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- 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
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Abstract
The application relates to the field of telephone communication, in particular to a deviation compensation 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, and carrying out inner product on the adaptive weight vector and the noise-containing input signal vector to generate an output signal; generating an estimated error signal from the output signal and generating an error squared median therefrom; calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively; calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value; calculating a deviation compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value; and updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimated error signal. The adverse effect caused by input noise is effectively reduced.
Description
Technical Field
The invention relates to the field of telephone communication, in particular to a deviation compensation echo cancellation method and 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. Conventional Least Mean Square (LMS) and Normalized Least Mean Square (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, a series of echo cancellation methods with anti-impulse noise, such as an error sign minimum mean square echo cancellation method, a mixed norm echo cancellation method, an echo cancellation method based on maximum correlation entropy, and the like, are proposed.
In addition, the conventional echo cancellation methods are designed based on standard regression models, that is, it is assumed that the input signal obtained by the echo cancellation method is the same as that of the unknown system. However, due to sampling errors and other reasons in practical application, the input signal is mixed with noise, so that the performance of the traditional echo cancellation method is greatly affected. For this problem, a deviation compensation method and an overall least square method are proposed.
At present, scholars propose a bias compensation maximum correlation entropy method (BC-MCC) and a bias compensation minimum error entropy method (BC-MEE), and although these methods have good anti-impulse noise effect under the model with error variables, instantaneous values of noise are required to be used when weight vectors are updated iteratively, however, the data cannot be obtained in practical application, and thus cannot be used for echo cancellation in telephone communication.
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 problem that although the impulse noise resistance effect under the error variable model is good in the prior art, the instantaneous value of noise is needed to be used when the weight vector is updated iteratively, but the data cannot be acquired in practical application, so that the invention cannot be used for echo cancellation in telephone communication.
To solve the above technical problem, a first aspect of the present invention provides a bias-compensated 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 estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
calculating an error signal variance estimation value and an adaptive weight vector variance power estimation value according to the error square median and the adaptive weight vector respectively;
Calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
Constructing an augmentation weight vector according to the self-adaptive weight vector;
calculating a bias compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value;
And updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
In one embodiment of the invention, the adaptive weight vector is expressed as:
；
Wherein is M self-adaptive weights, the subscript n represents time, and the superscript/> represents transposition operation;
The noisy input signal vector is expressed as:
；
Wherein is M sampling values of the noisy speech signal.
In one embodiment of the invention, the output signal is represented as:
；
Wherein is a noisy input signal vector,/> is an adaptive weight vector, and superscript/> represents a transpose operation.
In one embodiment of the invention, the estimation error signal is expressed as:
；
Wherein denotes the noise-containing echo signal at time n, i.e. the noise-containing desired signal.
In one embodiment of the invention, the squared error median is expressed as:
；
Wherein is an integer representing the length of the median filter.
In one embodiment of the invention, the error signal variance estimate is expressed as:
；
Wherein is a smoothing factor, the value range is 0.99 to 0.999, and the smoothing factor is used for smoothing estimation, and/() is an error signal variance estimation value at the time of n-1;
The adaptive weight vector power estimate is expressed as:
；
Wherein is a smoothing factor, the value range is 0.99 to 0.999, the smoothing factor is used for smooth estimation, and M is the length of the self-adaptive weight vector;
the input noise variance estimate is expressed as:
；
Wherein is the output noise to input noise variance ratio,/> is the variance of the measurement noise without impulse noise,/> is the variance of the input noise, and M is the length of the adaptive weight vector.
In one embodiment of the invention, the augmented weight vector is expressed as:
；
Where is the noise ratio, the superscript/> denotes the transpose operation.
In one embodiment of the invention, the bias compensation term is expressed as:
；
Wherein is a kernel width parameter with a positive number, and/() is a 2-norm.
In one embodiment of the present invention, the formula for updating the adaptive weight vector is:
；
Wherein is a step parameter with positive value.
A second aspect of the present invention provides a bias-compensated echo cancellation system for telephone communications, the system comprising: the system comprises a data acquisition module, a first construction module, a first calculation module, a second calculation module and a second construction module;
The data acquisition module is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller;
The first build module is configured to: 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 estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
The second computing module is configured to: calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively; calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
The second build module is configured to: constructing an augmentation weight vector according to the self-adaptive weight vector; calculating a bias compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value; and updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
Compared with the prior art, the technical scheme of the invention has the following advantages:
According to the deviation compensation echo cancellation method and system for telephone communication, the deviation compensation item is calculated, and the adaptive weight vector is updated through the deviation compensation item, so that the purpose of achieving stronger robustness and effectively reducing adverse effects caused by input noise on the premise of not outputting noise instantaneous values is achieved.
Drawings
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 bias-compensated echo cancellation method for telephone communications provided by the present invention;
FIG. 2 is a waveform diagram of an echo channel in a bias-compensated echo cancellation and system for telephone communications according to the present invention;
FIG. 3 is a waveform diagram of a voice signal in a bias-compensated echo cancellation method and system for telephone communications according to the present invention;
FIG. 4 is a graph of normalized mean square deviation when the signal-to-noise ratio of input noise is 10dB in a deviation compensated echo cancellation method and system for telephone communication provided by the invention;
fig. 5 is a block diagram of an offset compensating echo cancellation system for telephone communications 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 bias-compensated echo cancellation method for telephone 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, the/> adaptive weights/> of the echo canceller at the current moment are obtained, and the sampling values/> of the noisy speech signals at the current/> moment and the consecutive/> moments before the current/> moment are obtained.
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, by self-adaptation weights/> constitute, the superscript/> represents the transposition operation;
The noisy input signal vector is expressed as:
（2）；
Wherein, it is made up of noise-containing speech signal sample .
In an actual application scene, adaptive weights/> of an echo canceller at n time are combined to form an adaptive weight vector, and sampling values/> of noise-containing voice signals at n time and the previous continuous/> time are formed into a noise-containing 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 is a noisy input signal vector, and/() is an adaptive weight vector.
In an actual application scene, the output signal/> of the echo canceller at the time n is obtained by performing inner product on the noisy input vector and the adaptive weight vector/> .
S400, generating an estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
In step S400, the estimation error signal is expressed as:
（4）；
Wherein denotes the noise-containing echo signal at time n, i.e. the noise-containing desired signal. The median error squared is expressed as:
（5）；
Wherein is an integer representing the length of the median filter.
In an actual application scene, an estimated error signal at the time n is obtained through calculation according to an output signal and a formula (4), and then whether median filtering is carried out or not is determined according to whether the estimated error signal at the time is interfered by impulse noise or not, namely if/> , an error square median is obtained through calculation according to a formula (5), otherwise, the error square median is/> , wherein/> is an estimated value of error signal variance at the time/> ; the/> is a threshold parameter for determining whether or not it is affected by impulse noise, and may be typically between 9 and 25.
S500, calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively;
in step S500, the error signal variance estimation value is expressed as:
（6）；
wherein is a smoothing factor, the value range is 0.99 to 0.999, and the smoothing factor is used for smoothing estimation, and/() is an error signal variance estimation value at the time of n-1;
The adaptive weight vector variance estimation value is expressed as:
（7）；
Wherein is a smoothing factor, the value range is 0.99 to 0.999, the smoothing factor is used for smoothing estimation, and M is the length of the self-adaptive weight vector.
In an actual application scenario, an error signal variance estimation value at time n is calculated according to equation (6). Where/> is a smoothing factor, typically between 0.99 and 0.999 can be taken for smoothing the estimation, ensuring the accuracy of the estimation. And calculating the adaptive weight vector variance estimation value/> of the echo canceller at the time n according to the formula (7).
S600, calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
in step S600, the input noise variance estimation value is expressed as:
（8）；
Wherein is the output noise to input noise variance ratio,/> is the variance of the measurement noise without impulse noise,/> is the variance of the input noise, and M is the length of the adaptive weight vector.
In an actual application scenario, the error signal variance estimation value and the adaptive weight vector variance estimation value obtain an input noise variance of the echo canceller at the n time according to equation (8).
S700, constructing an augmentation weight vector according to the self-adaptive weight vector;
in step S700, the augmented weight vector is expressed as:
（9）；
Where is the noise ratio, the superscript/> denotes the transpose operation.
In an actual application scene, an augmented weight vector is constructed according to the noise ratio and the adaptive weight vector.
S800, calculating a deviation compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value;
in step S800, the deviation compensation term is expressed as:
（10）；
Wherein is a kernel width parameter with a positive number, and/() is a 2-norm.
In an actual application scene, the deviation compensation term is obtained by a method of converting expectations into integral solutions by utilizing a joint Gaussian distribution relation satisfied between estimation errors and noisy inputs, namely an initial deviation compensation term is and is used for compensating the estimation deviation of an echo channel introduced by input noise, wherein/> is a kernel width parameter and is used for controlling the resistance to impulse noise interference; the/> is the variance of the input noise; the/> is the echo channel weight vector to be estimated; the vector of amplified echo channel weights is,/> , the noise ratio is,/> , and the variance of the measured noise in the desired signal without impulse noise interference is,/> . The bias compensation term/> is obtained by replacing the input noise variance/> and the echo channel weight vector in the initial bias compensation term/> with the input noise variance/> and the adaptive weight vector/> at time n, respectively.
And S900, updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
In step S900, the formula for updating the adaptive weight vector is:
（11）；
Wherein is a step parameter with positive value.
In an actual application scenario, the weight vector of the echo canceller is updated according to an iterative equation (11), where is a step size for controlling the convergence rate. The embodiment adopts a computer experiment method to verify the performance of the IBC-MCC echo cancellation method applied to the echo cancellation task in telephone communication and is the same as the prior method: the experimental results of the bias-compensated least mean square (BC-LMS) echo cancellation method and the maximum correlation entropy (MCC) echo cancellation method are compared.
Experiments were performed in an echo cancellation environment in telephone communications using the echo channel shown in fig. 2 as an unknown system and the speech signal shown in fig. 3 as an input signal for the echo channel. The input noise is Gaussian white signal and the signal-to-noise ratio is 10dB. The measurement noise is generated by a mixed Gaussian model, namely , wherein/> is zero-mean Gaussian white noise meeting the signal-to-noise ratio of 30dB,/> is impulse noise, the measurement noise is generated by a Bernoulli Gaussian process, namely/> ,/> is a Bernoulli process, the probability of taking 0 is 0.95, the probability of taking 1 is 0.05, and/> is zero-mean Gaussian white noise meeting the signal-to-noise ratio of-10 dB. Normalized Mean Square Deviation (NMSD) is used as a performance measure, i.e./> , in dB, where log represents logarithm and/> represents echo channel. The NMSD curves simulated in the figure are all obtained by averaging 100 independent iterations. As can be seen from fig. 4, the IBC-MCC echo cancellation method according to the embodiment of the present application can effectively cancel echo in a pulse environment with noise.
In a second aspect, and with reference to fig. 5, the present application provides a bias-compensated echo cancellation system for telephone communications, the system comprising: a data acquisition module 100, a first construction module 200, a first calculation module 300, a second calculation module 400, and a second construction module 500;
the data acquisition module 100 is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller;
The first building block 200 is configured to: 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 300 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 estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
The second computing module 400 is configured to: calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively; calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
The second building module 500 is configured to: constructing an augmentation weight vector according to the self-adaptive weight vector; calculating a bias compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value; and updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
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 (10)
1. A bias-compensated echo cancellation method for telephone 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 estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
Calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively;
Calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
Constructing an augmentation weight vector according to the self-adaptive weight vector;
calculating a bias compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value;
And updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
2. A bias-compensating echo cancellation method for telephone communications as claimed in claim 1, wherein:
The adaptive weight vector is expressed as:
；
Wherein is M self-adaptive weights, the subscript n represents time, and the superscript/> represents transposition operation;
The noisy input signal vector is expressed as:
；
Wherein is M sampling values of the noisy speech signal.
3. A bias-compensating echo cancellation method for telephone communications according to claim 2, wherein:
The output signal is expressed as:
；
wherein is a noisy input signal vector,/> is an adaptive weight vector, and superscript/> represents a transpose operation.
4. A bias-compensating echo cancellation method for telephone communications according to claim 3, wherein:
the estimated error signal is expressed as:
；
Wherein denotes the noise-containing echo signal at time n, i.e. the noise-containing desired signal.
5. A bias-compensating echo cancellation method for telephone communications as claimed in claim 4, wherein:
The median error squared is expressed as:
；
Wherein is an integer representing the length of the median filter.
6. A bias-compensated echo cancellation method for telephone communications as claimed in claim 5, wherein:
The error signal variance estimate is expressed as:
；
Wherein is a smoothing factor, the value range is 0.99 to 0.999, and the smoothing factor is used for smoothing estimation, and/() is an error signal variance estimation value at the time of n-1;
The adaptive weight vector power estimate is expressed as:
；
wherein is a smoothing factor, the value range is 0.99 to 0.999, the smoothing factor is used for smooth estimation, and M is the length of the self-adaptive weight vector;
the input noise variance estimate is expressed as:
；
Wherein is the output noise to input noise variance ratio,/> is the variance of the measurement noise without impulse noise, is the variance of the input noise, and M is the length of the adaptive weight vector.
7. A bias-compensating echo cancellation method for telephone communications as claimed in claim 6, wherein:
The augmented weight vector is expressed as:
；
where is the noise ratio, the superscript/> denotes the transpose operation.
8. A bias-compensating echo cancellation method for telephone communications as claimed in claim 7, wherein:
The bias compensation term is expressed as:
；
Wherein is a kernel width parameter with a positive number, and/() is a 2-norm.
9. A bias-compensating echo cancellation method for telephone communications as claimed in claim 8, wherein:
The formula for updating the self-adaptive weight vector is as follows:
；
Wherein is a step parameter with positive value.
10. A bias-compensating echo cancellation system for telephone communications, the system comprising: the system comprises a data acquisition module, a first construction module, a first calculation module, a second calculation module and a second construction module;
The data acquisition module is configured to: acquiring an adaptive weight value and a noise-containing voice signal sampling value of an echo canceller;
The first build module is configured to: 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 estimated error signal according to the output signal, and generating an error square median according to the estimated error signal;
The second computing module is configured to: calculating an error signal variance estimation value and an adaptive weight vector power estimation value according to the error square median and the adaptive weight vector respectively; calculating an input noise variance estimation value according to the error signal variance estimation value and the self-adaptive weight vector power estimation value;
The second build module is configured to: constructing an augmentation weight vector according to the self-adaptive weight vector; calculating a bias compensation term according to the augmentation weight vector, the self-adaptive weight vector and the input noise variance estimation value; and updating the self-adaptive weight vector according to the deviation compensation term, the noisy input signal vector and the estimation error signal.
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