Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation
This is an incremental improvement for acoustic echo cancellation systems, enhancing performance in sparse scenarios.
The paper tackled acoustic echo cancellation by proposing the S-IWF-SSAF algorithm with a joint optimization scheme for step-size and sparsity penalty, resulting in improved convergence rate and steady-state error in sparse scenarios compared to the IWF-SSAF algorithm.
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.