Base On Proportionate Adaptive Robust Echo Cancellation Algorithm

Sparse adaptive filters with lots of coeffcients are usually involved in both acoustic and network echo cancellation, because the impulse response of echo path is long and sparse. The core of the sparse adaptive filter is the proportionate-type algorithm which takes advantage of sparsity in impulse response, and has applied in echo cancellation widely. The problem of echo cancellation and proportionate-type algorithm are analysed in this thesis detailedly.In this thesis, we improved the update rule of proportionate matrix in MPNLMS, and used a new piecewise linear function instead of the logarithmic function, then get the improved segment MPNLMS(ISMPNLMS) has faster converge speed.In order to get the good performce in an impulsive noise environment, we proposed proportionate-type normalized least mean m-estimate algorithms (PNLMMs) by modified Huber function. Inspired by PNLMMs algorithm, we proposed a novel based M-estimation proportionate-type affine projection algorithm (PAPA-Ms) by a new score function. In the simulation, PNLMMs and PAPA-Ms show the good convergence performance in impulsive noise environments. In addition, verify the effectiveness of the PNLMMs and PAPA-Ms by Geigel double-talk detection during the double-talk.