论文标题
具有单个加权因子的稀疏感知SSAF算法，以取消声音回声
Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation
论文作者
论文摘要
在本文中，我们提出和分析具有单个加权因子（S-IWF-SSAF）算法的稀疏性符号亚带自适应滤波，并考虑其在声学回声取消（AEC）中的应用。此外，我们设计了阶梯尺寸和稀疏惩罚参数的联合优化方案，以增强S-IWF-SSAF性能，以收敛速率和稳态误差。理论分析表明，在稀疏场景中，S-IWF-SSAF算法的表现优于先前的符号亚带自适应滤波，该算法具有单个加权因子（IWF-SSAF）算法。特别是，与IWF-SSAF算法的现有分析相比，所提出的分析不需要大量子带，长期自适应过滤器和支核分析过滤器库的假设，并且与模拟结果匹配。在系统识别和AEC情况下的模拟都证明了我们的理论分析以及所提出的算法的有效性。
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.
