Channel Sparsity Aware Function Expansion Filters Using the RLS Algorithm for Nonlinear Acoustic Echo Cancellation

Abstract

In this paper, we propose a channel sparsity aware sequential recursive least squares (sparse SEQ-RLS) algorithm for function expansion filters with applications in nonlinear echo cancellation. The algorithm is developed based on a diagonal channel structure from the Volterra filter and updating dominant coefficients taking into consideration of sparse elements in the diagonal channel. The third-order Volterra, third-order even mirror Fourier nonlinear (EMFN), and functional link artificial neural network (FLANN) filters are developed according to the sparse SEQ-RLS algorithm. The computation complexity for the upper bound is analyzed to validate the efficiency for each proposed filter. Computer simulation results demonstrate that all proposed function expansion filters with the sparse SEQ-RLS algorithm are effective for nonlinear echo cancellation. In general, the EMFN filter provides better performance compared to the Volterra and FLANN filters.

Keywords

• Nonlinear sparse system modeling
• nonlinear acoustic echo cancellation
• sparse sequential RLS algorithm
• Volterra filter
• even mirror Fourier nonlinear filter