Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

TL;DR

This paper introduces a new normalized subband adaptive filter algorithm for acoustic echo cancellation that leverages proximal methods and adaptive thresholding, demonstrating improved performance through simulations.

Contribution

A novel proximal normalized subband adaptive algorithm combining sparsity-aware and proportionate mechanisms with adaptive thresholding, tailored for acoustic echo cancellation.

Findings

Outperforms existing algorithms in acoustic echo cancellation tasks.

Adaptive thresholding improves convergence and steady-state performance.

Validated through simulations in system identification and echo cancellation contexts.

Abstract

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.