Fast least-squares (LS) in the voice echo cancellation application

Abstract

The existing echo cancellation methods are primarily based on the LMS adaptive algorithm. Despite the fact that the LMS echo canceller works better than its predecessor-the echo suppressor, its performance can be substantially improved if the Recursive LS (RLS) algorithm is used instead. However the αp2operations (p: filter order) per sample required prevents the RLS algorithm from being used in this and many other applications where the filter order is relatively high. The computational complexity of the RLS has recently been brought down to αp by exploiting the shifting structure of the signal covariance matrix. Two algorithms, namely the LS lattice and the fast Kalman, are used here. Comparisons between the two LS algorithms and the LMS gradient algorithm are made and the performance difference is demonstrated. Two important problems in voice echo cancellation: the flat delay estimation and the near-end speech detection, are approached novelly through a minimum-mean-squared-error flat delay estimator and a likelihood near-end speech detector. Simulation results are very satsifactory.