Variable-forgetting factor RLS for stereophonic acoustic echo cancellation with widely linear model

Abstract

A widely linear (WL) model was recently proposed for stereophonic acoustic echo cancellation (SAEC). In this framework, the classical two-input/two-output SAEC scheme with real random variables is recasted as a single-input/single-output system with complex random variables. The main advantage of this approach is that instead of handling two (real) output signals separately, we only handle one (complex) output signal. In general, due to their good convergence features, recursive least-squares (RLS) algorithms are preferable for SAEC applications. However, the performance of RLS-based algorithms is governed by the forgetting factor, whose value leads to a compromise between convergence rate/tracking capabilities on the one hand and misadjustment/stability on the other hand. In this paper, we develop a variable-forgetting factor RLS (VFF-RLS) algorithm for SAEC with the WL model. Simulation results indicate the good performance (in terms of tracking and robustness) of the proposed algorithm.