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.

中文翻译：

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具有单个加权因子的稀疏感知 SSAF 算法：声学回声消除的性能分析和改进

摘要 在本文中，我们提出并分析了具有单个加权因子的稀疏感知符号子带自适应滤波（S-IWF-SSAF）算法，并考虑了其在声学回声消除（AEC）中的应用。此外，我们设计了步长和稀疏惩罚参数的联合优化方案，以提高 S-IWF-SSAF 在收敛速度和稳态误差方面的性能。理论分析表明，S-IWF-SSAF算法在稀疏场景下的性能优于之前的带个体加权因子的符号子带自适应滤波（IWF-SSAF）算法。特别是，与现有的 IWF-SSAF 算法分析相比，该分析不需要大量子带、长自适应滤波器和超幺正分析滤波器组的假设，并与模拟结果吻合良好。在系统识别和 AEC 情况下的仿真证明了我们的理论分析和所提出算法的有效性。