Echo cancellation method based on convex combination for M-estimaion proportional affine projection

Abstract

The invention relates to an echo cancellation method based on convex combination for M-estimation proportional affine projection. The method comprises steps of A of far-end signal sampling; B of convex combination, a large-step filter value y1(n) and a small-step filter value y2(n) at the current time n are subjected to convex combination through a large-step filter weight lambda(n) of the currenttime n to obtain a combination filter value y(n) of the current time n; C of echo signal elimination, an echoed near-end signal d(n) picked up by a near-end microphone is subtracted with an output value y(n) of an adaptive filter and then returned to a far end, a return signal is a residual signal e(n), and e(n)=d(n)-y(n); D of updating a filter tap weight vector, the method of M-estimation proportional affine projection based on convex combination is utilized to calculate update of an adaptive filter tap weight vector; E of updating a filter weight; F of limiting the filter weight; G, let n=n+1, and the steps A, B, C, D, E, F, G are repeated till the end of iteration.

Description

technical field [0001] The invention relates to an adaptive echo cancellation method for a communication system. 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 voice. Therefore, effective measures must be ta...

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 filter weight update parameters that are positively correlated with the input vector, thereby accelerating the convergence speed of the algorithm when identifying a sparse system; Through the idea of ​​M estimation, a threshold parameter is set. 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 a good performance. Anti-impulse noise ability; but this method only considers the improvement of filter weight update parameters, but the update step size of its adaptive filter is fixed, the step size used is large, the convergence speed is fast, and the steady-state error is also large ; If the step size is small, the steady-state error will be small and the convergence speed will be slow; the problem of convergence speed and steady-state error cannot be considered

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