Fast System Identification Using Affine Projection and a Critically Sampled Subband Adaptive Filter

Abstract

This paper investigates the use of a subband affine projection (AP) algorithm for solving system identification problems using critically sampled subband adaptive filters. The subband AP is first analyzed with respect to theoretical rate of convergence and computational complexity. Simulation results for the algorithm are presented in the context of measuring a room impulse response for acoustic echo cancellation and tracking changes to the impulse response over time. In these simulations, the subband AP is compared to other subband adaptation algorithms using two-, four-, and eight-channel filter banks and to fullband adaptation algorithms. To evaluate the algorithm in a practical implementation, experimental results are presented for echo cancellation using speech input signals in a conference room. It is shown that a four-channel filter bank with subband AP can achieve an average mean square error that is 5 dB lower than a subband normalized least-mean-square algorithm during initial filter convergence