Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation

TL;DR

This paper introduces a sparsity-aware adaptive filtering algorithm with individual weighting factors for acoustic echo cancellation, improving convergence and steady-state performance in sparse scenarios.

Contribution

It proposes the S-IWF-SSAF algorithm with a joint optimization scheme, and provides a theoretical analysis that outperforms previous methods without restrictive assumptions.

Findings

Outperforms previous IWF-SSAF in sparse scenarios

Theoretical analysis matches simulation results

Effective in acoustic echo cancellation and system identification

Abstract

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.