Proximal Normalized Subband Adaptive Filtering for Acoustic Echo Cancellation
Proximal Normalized Subband Adaptive Filtering for Acoustic Echo Cancellation
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DOI：
10.1109/taslp.2021.3087951
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Gang Guo;Yi Yu;R. Lamare;Zongsheng Zheng;Lu Lu-Lu;Qiangming Cai
中科院分区：
文献类型：
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作者：
Gang Guo;Yi Yu;R. Lamare;Zongsheng Zheng;Lu Lu-Lu;Qiangming Cai
在本文中，我们提出了一种新的归一化子带自适应滤波算法适用于稀疏场景，它结合了比例和稀疏意识的机制。该算法是基于最接近的前后向分裂和软阈值的方法。我们分析了该算法的平均值和均方行为，这是由模拟支持。此外，还提出了一种基于均方差最小化的最接近阈值参数的自适应选择方法。在系统辨识和声学回声消除的背景下的仿真验证了所提出的算法优于其同行。
In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.