基于稀疏性的个体加权SSAF声学回声消除算法
Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation
摘要：
本文提出并分析了稀疏性感知的符号子带自适应滤波算法（S-IWF-SSAF），并将其应用于声学回声消除（AEC）中。此外，我们设计了一个联合优化方案的步长和稀疏惩罚参数，以提高S-IWF-SSAF的收敛速度和稳态误差方面的性能。理论分析表明，S-IWF-SSAF算法在稀疏情况下的性能优于已有的符号子带独立加权因子自适应滤波（IWF-SSAF）算法。特别是，与已有的IWF-SSAF算法分析相比，该分析不需要大量子带、长自适应滤波器和仿酉分析滤波器组的假设，与仿真结果吻合较好。在系统辨识和AEC两种情况下的仿真结果验证了我们的理论分析和所提算法的有效性。
英文摘要：
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.
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