A proportional affine projection-like adaptive echo cancellation method based on M estimation is proposed

Abstract

The invention discloses a proportional affine projection-like adaptive echo cancellation method based on M estimation. The method comprises: A,a far end signal is collected; B, an echo is estimated, wherein the input vector X (n) of the filter is passed through an adaptive filter to obtain an output value y (n), i. E., an estimate value y (n) of the echo, y (n) = WT (n) X (n); C, echo is cancelled, the output value y (n) of the adaptive filter is subtracted from the near-end signal d (n) picked up by the near-end microphone and sent back to the far-end, and the return signal is the residual signal e (n), e (n) = d (n)- Y (n); D, the filter tap weight vector is updated, Using the method of proportional affine projection-like based on M estimation, the adaptive filter tap amount W (n) at time n is calculated, E, n is set as n+1, the steps of A, B, C, D are repeated until the call ends. The method is effective in eliminating the acoustic echo of communication system with fast convergencespeed and small steady-state error.

Description

technical field [0001] The invention relates to an adaptive echo cancellation method, which belongs to the technical field of communication echo cancellation. Background technique [0002] In a voice-based communication system (such as a hands-free phone, a video conference system, etc.), the voice quality is usually affected by the echo, mainly the acoustic echo, which seriously affects the call quality. Echo is a phenomenon in which sound or signal is reflected back to the signal source after delay or deformation. In communication systems such as voice communication, data communication, satellite communication, hands-free phone, and teleconferencing system, echo phenomenon exists to varying degrees. . Take video conference as an example, because the speaker and the microphone are placed in the same space, the far-end voice from the local speaker is received by the local near-end microphone and transmitted back to the far-end, causing the far-end speaker to hear his own vo...

AI Technical Summary

Problems solved by technology

This method multiplies the input vector by a proportional matrix that is positively correlated with the input vector, so that the input signal is assigned to different adaptive filters. Speed; through the idea of ​​M estimation, set a threshold parameter. When the error is smaller than the threshold parameter, the weight vector is updated normally. When the error is greater than the threshold parameter, the threshold parameter replaces the error to update the weight vector, so that the algorithm has Good anti-impulse noise ability; but when it updates the weight vector, the input error signal is only the error signal at the current moment, and it is only the input vector at the current moment. For severe autocorrelation signals, it does not make full use of the phase The relevant information in the signal in the first few moments of the neighbor will lead to a serious decrease in the convergence speed, and this situation often occurs in speech input signals; therefore, its convergence speed needs to be improved

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