CN112037811A - Function connection type self-adaptive nonlinear echo cancellation method - Google Patents
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Abstract
The invention relates to the technical field of echo cancellation, and discloses a function connection type self-adaptive nonlinear echo cancellation method which is used for improving the echo cancellation effect. The invention carries out sigmoid nonlinear transformation processing and function connection expansion processing on the far-end input signals in an echo path, respectively filters the processed signals through a self-adaptive filter, and updates the weight of the self-adaptive filter according to the filtering result; wherein the parameter values of the sigmoid transformation are based on the filtering result of the adaptive filter and the weight estimation. The invention is suitable for nonlinear echo cancellation processing.
Description
Technical Field
The invention relates to the technical field of echo cancellation, in particular to a function connection type self-adaptive nonlinear echo cancellation method.
Background
In recent years, communication and network technologies have been developed, so that new requirements are made on the quality of communication, the most important of which is the quality of voice call. Acoustic echo is one of the important factors that degrade the quality of a voice call. The echo is generated due to the impulse response coupling effect of the echo path between the microphone and the loudspeaker. Since the human ear is very sensitive to echo, echo delayed for more than 10ms can be captured by the human ear, and echo delayed for more than 32ms can seriously interfere with the speech quality. Therefore, how to eliminate the acoustic echo has important practical significance. The most commonly applied method at present is an adaptive filtering based Echo canceller (AEC). The basic principle of AEC is to estimate the impulse response of the echo path using an adaptive filtering algorithm, which is essentially a problem for system identification. However, when the echo path has non-linear characteristics, the performance of the conventional adaptive filtering algorithm, such as Least Mean Square (LMS) algorithm and Normalized Least Mean Square (NLMS) algorithm, is significantly degraded. Therefore, how to model the nonlinear characteristics in the echo path has great significance in providing an effective nonlinear echo cancellation method.
The current nonlinear echo cancellation methods are typically as follows:
(1) function-connected adaptive filter (SFLAF) for nonlinear echo cancellation
Refer to the conventional method (1) "Functional Link Adaptive Filters for Nonlinear Acoustic Echo Cancellation" (Comminiello D, IEEE Transactions on Audio, Speech, and Language Processing,2013,21(7): 1502-. The method is characterized in that nonlinear expansion is carried out on input signals, specifically, the input signals are connected and expanded through a trigonometric function, and the untransformed signals and the transformed signals are filtered by two filters respectively, so that the aim of simultaneously modeling the linear and nonlinear characteristics of an echo path is fulfilled.
(2) Nonlinear echo canceller (RLS-sigmoid) using a combination of sigmoid transformation and RLS algorithm
Reference is made to the prior art method (2) "A Nonlinear Acoustic Echo Canceller Using Signal Transformer in connection With RLS Algorithm" (Fu J, IEEE Transactions on Circuits and Systems Ii-express Briefs,2008,55(10): 1056-. The method sets the nonlinear transformation in the echo path as sigmoid transformation, estimates the sigmoid parameters before filtering, and can quickly converge the estimated value to the true value by combining the quick convergence and strong tracking capability of RLS.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a function-connected adaptive non-linear echo cancellation method is provided to improve the echo cancellation effect.
In order to achieve the purpose, the invention adopts the technical scheme that: the two methods are combined to improve the function connection type self-adaptive echo canceller: the RLS-sigmoid method of the method (2) is introduced into the SFLAF linear filter in the method (1), the far-end input signal is set to be sigmoid type conversion when nonlinear conversion is carried out, and the parameter value of the sigmoid conversion is estimated by adopting the RLS-sigmoid, so that the nonlinear modeling capability of the algorithm is further enhanced, and the echo cancellation effect is improved.
The specific implementation mode of the invention comprises the following steps: respectively carrying out sigmoid nonlinear transformation processing and function connection expansion processing on a far-end input signal in an echo path, respectively filtering the processed signal through a self-adaptive filter, and updating the weight of the self-adaptive filter according to a filtering result; wherein the parameter value of sigmoid nonlinear transformation is based on the filtering result of the adaptive filter and the weight estimation.
Specifically, the sigmoid nonlinear transformation formula is as follows:
wherein alpha and beta are parameter values of sigmoid nonlinear transformation, x is a signal before transformation, and f (x) is a signal after transformation.
Further, the step of estimating parameter values of sigmoid nonlinear transformation of the invention may comprise:
determining initial parameter values of alpha and beta;
subtracting the general filtering value of the adaptive filter from the near-end input signal in the echo path to obtain a residual signal e (n);
iteratively updating alpha and beta based on the weight of the adaptive filter, the initial parameter values of alpha and beta and the residual signal e (n), wherein the formula of iterative updating is as follows;
wherein, muαAnd muβIs two step parameters of alpha and beta in updating, T represents transposition, n represents time, u (n) represents input signal, wS(n) is the weight of the filter.
Further, for the signal after sigmoid nonlinear transformation processing, the weight w of the adaptive filterS(n) is updated using a recursive least squares algorithm, the updated formula can be as follows:
P(0)＝-1I
wS(n+1)＝wS(n)+k(n)e(n)
P(n+1)＝λ-1P(n)-λ-1k(n)uT(n)P(n)
where matrix P is an inverse correlation matrix, I is an identity matrix, k is a gain vector, and λ and are the forgetting factor and the regularization parameter, respectively.
Further, for the signal after sigmoid nonlinear transformation processing, the weight w of the adaptive filterFL(n) is updated using a normalized least mean square algorithm, the update formula of which can be as follows:
wherein, muFLAnd σ are the step size and the regularization parameter, respectively. .
The invention has the beneficial effects that: the invention sets the nonlinear transformation in the echo path as sigmoid transformation, and introduces RLS-sigmoid in the function connection type self-adaptive canceller to estimate parameter values alpha and beta of the sigmoid transformation. Compared with the independent action of the two methods, the combination of the two methods further improves the modeling capability of the nonlinear characteristic of the echo path, and has faster convergence speed and better echo cancellation effect.
Drawings
FIG. 1 is a block flow diagram of an embodiment of the present invention;
fig. 2 is a room impulse response used in the simulation experiment.
Fig. 3 is a process of estimating parameters of sigmoid nonlinear transformation in an echo path by the prior art method (2) and the embodiment of the present invention in a simulation experiment.
FIG. 4 is a graph comparing the lift-back return loss enhancement (ERLE) of prior art methods (1), (2) and embodiments of the present invention in a simulation experiment.
Numbering in the figures: s1 and S2 are respectively iteration curves of estimated values of the parameters alpha and beta of the invention, S1 and S2 are respectively iteration curves of estimated values of the parameters alpha and beta of the existing method (2), D1 is a boost return loss enhancement curve of the invention, D2 is a boost return loss enhancement curve of RLS-sigmoid, and D3 is a boost return loss enhancement curve of SFLAF.
Detailed Description
In order to improve the effect of echo cancellation, the invention discloses a function connection type self-adaptive nonlinear echo cancellation method, which comprises the steps of respectively carrying out sigmoid nonlinear transformation processing and function connection expansion processing on a far-end input signal in an echo path, respectively filtering the processed signal through a self-adaptive filter, and updating the weight of the self-adaptive filter according to a filtering result, wherein the parameter value of the sigmoid transformation is estimated based on the filtering result and the weight of the self-adaptive filter. The present invention will be described in detail below with reference to examples and the accompanying drawings.
As shown in fig. 1, the embodiment provides an improved adaptive nonlinear echo cancellation method of function connection type, which includes the following specific steps:
A. collecting remote input signals
Sampling a far-end signal in an echo path to obtain a discrete value u (n) of a current moment n of the signal, wherein the value of the far-end signal from n to n-L +1 forms a filter input vector u (n) of the current moment n, and u (n) is [ u (n) ],]Twhere L300 is the number of filter taps, superscript T generationTable transposition;
B. far-end signal sigmoid nonlinear transformation
Setting initial parameter values of sigmoid nonlinear transformation: α (n) ═ 2, β (n) ═ 2; transforming the input vector u (n) by sigmoid:
to obtain uS(n), where α and β are the parameter values of sigmoid transformation, x is the signal before transformation, and f (x) is the signal u after transformationS(n)；
C. Remote signal function connection extension
The input vector u (n) is extended by a trigonometric function concatenation as follows:
wherein i, j and Q are auxiliary signs, j and Q are limited by Q, i is limited by L, Q is more than or equal to 1 and less than or equal to Q, Q is the order of function connection expansion, and i is more than or equal to 0 and less than or equal to L-1; obtaining an expanded input signal
D. Far-end signal filtering
Respectively processing the signals u obtained in the step BS(n) processing with step C to obtain signal uFL(n) obtaining a filtered signal by an adaptive filter:
wherein, wS(n) and wFL(n) is the weight of the corresponding filter, and the initial value is 0; the overall filtered output is then obtained: y (n) ═ yS(n)+yFL(n)；
E. Echo cancellation
The near-end signal d (n) picked up by near-end microphone and with echo is subtracted from total filtering value y (n) and then returned to far-end, the returned signal is total residual signal e (n), e (n) ═ d (n) -y (n)
F. Updating estimation values of sigmoid nonlinear transformation parameters alpha and beta
And updating the parameters of sigmoid transformation of the next iteration by using a residual signal e (n) according to the following method:
wherein, muαAnd muβIs two step size parameters, wS(n) is the weight of the filter;
G. weight update for filters
For the signal after sigmoid nonlinear transformation processing, the weight w of the adaptive filterS(n) updating with a recursive least squares algorithm (RLS):
P(0)＝-1I
wS(n+1)＝wS(n)+k(n)e(n)
P(n+1)＝λ-1P(n)-λ-1k(n)u T(n)P(n)
wherein, the matrix P is an inverse correlation matrix, I is an identity matrix, k is a gain vector, and λ and are a forgetting factor and a regularization parameter respectively;
for the signal after sigmoid nonlinear transformation processing, the weight w of the adaptive filterFL(n) updating with a normalized least mean square algorithm (NLMS):
wherein, muFLAnd σ are the step size and the regularization parameter, respectively;
H. let n be n +1, repeat the step of A, B, C, D, E, F, G until the iteration ends.
Simulation experiment
To verify the effectiveness of embodiments of the present invention, simulation experiments were performed and compared to prior method (1) (SFLAF) and prior method (2) (RLS-sigmoid).
The far-end signal u (n) of the simulation experiment is generated by a first-order autoregressive model (AR) and has a transfer function of:
where θ is 0.8. The echo channel impulse response is obtained in a quiet closed room with the height of 3.8m, the width of 3m, the length of 4.6m, the temperature of 20 ℃ and the humidity of 50 percent, and the impulse response length is 300. The resulting impulse response is shown in figure 2. In the room, a near-end signal d (n) of 20000 time points is picked up by a microphone with a sampling frequency of 8000 Hz. The background noise for the additional experiment was white gaussian noise of 50 dB. The order Q of the trigonometric function connection extension is 1. The true value of sigmoid nonlinear transformation is: α is 4 and β is 3. The simulation experiment results were averaged over 100 independent runs.
According to the above experimental conditions, the echo cancellation experiment is performed by using the embodiment of the method of the present invention and the existing two methods. The parameters of each method are specifically taken as shown in table 1.
Table 1 optimal parameter approximation for each algorithm
Fig. 3 shows an iterative plot of the estimated values of the parameters α and β of sigmoid nonlinear transformation according to an embodiment of the present invention and the prior art method (2). It can be seen that both methods can converge to the true parameter value, and the convergence speed of the present invention is faster.
Fig. 4 shows the contrast curves of Echo Return Loss Enhancement (ERLE) for the 3 methods. It can be seen that RLS-sigmoid can accurately estimate the parameter value of sigmoid nonlinear transformation, and the effect is superior to SFLAF. The invention is combined with SFLAF on the basis of RLS-sigmoid, thereby further accelerating the convergence process of the sigmoid nonlinear transformation parameter value. Therefore, compared with the methods of the prior methods (1) and (2), the convergence speed of the embodiment of the invention is faster, and the ERLE value after the convergence is stable is larger.
Claims (5)
1. A function connection type self-adaptive nonlinear echo cancellation method is characterized in that far-end input signals in an echo path are respectively subjected to sigmoid nonlinear transformation processing and function connection expansion processing, the processed signals are respectively filtered through a self-adaptive filter, and the weight of the self-adaptive filter is updated according to a filtering result; wherein the parameter value of sigmoid nonlinear transformation is based on the filtering result of the adaptive filter and the weight estimation.
2. The method of claim 1, wherein sigmoid nonlinear transformation is formulated as:
wherein alpha and beta are parameter values of sigmoid nonlinear transformation, x is a signal before transformation, and f (x) is a signal after transformation.
3. The method of claim 2, wherein the step of estimating the parameter values of the sigmoid nonlinear transformation comprises:
determining initial parameter values of alpha and beta;
subtracting the general filtering value of the adaptive filter from the near-end input signal in the echo path to obtain a residual signal e (n);
iteratively updating alpha and beta based on the weight of the adaptive filter, the initial parameter values of alpha and beta and the residual signal e (n), wherein the formula of iterative updating is as follows;
wherein, muαAnd muβIs two step parameters of alpha and beta in updating, T represents transposition, n represents time, u (n) represents input signal, wS(n) is the weight of the adaptive filter.
4. A method as claimed in claim 3, wherein the weights w of the adaptive filter are applied to the sigmoid nonlinear transformed processed signalS(n) updating by using a recursive least squares algorithm, wherein the updating formula is as follows:
P(0)＝-1I
wS(n+1)＝wS(n)+k(n)e(n)
P(n+1)＝λ-1P(n)-λ-1k(n)uT(n)P(n)
where matrix P is an inverse correlation matrix, I is an identity matrix, k is a gain vector, and λ and are the forgetting factor and the regularization parameter, respectively.
5. A method as claimed in claim 3, wherein the weights w of the adaptive filter are applied to the sigmoid nonlinear transformed processed signalFL(n) is updated by using a normalized least mean square algorithm, and the updating formula is as follows:
wherein, muFLAnd σ are the step size and the regularization parameter, respectively.
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| CN112882053A (en) * | 2021-01-21 | 2021-06-01 | 清华大学深圳国际研究生院 | Method for actively calibrating external parameters of laser radar and encoder |
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| CN113114865A (en) * | 2021-04-09 | 2021-07-13 | 苏州大学 | Combined function linkage type kernel self-response nonlinear echo cancellation method |
| CN112803921A (en) * | 2021-04-13 | 2021-05-14 | 浙江华创视讯科技有限公司 | Adaptive filter, method, medium, and electronic device |
| CN113113038A (en) * | 2021-05-19 | 2021-07-13 | 北京字节跳动网络技术有限公司 | Echo cancellation method and device and electronic equipment |
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