为加权预测误差算法使用分组稀疏惩罚进行麦克风子集选择
Microphone Subset Selection for the Weighted Prediction Error Algorithm using a Group Sparsity Penalty
AI总结：
本文提出使用分组稀疏惩罚改进加权预测误差算法，通过选择麦克风子集实现高效的去回声性能。
摘要：
回声可以严重降低在密闭空间中使用麦克风录制的语音信号质量。在具有空间分布麦克风的声学传感器网络中，仅使用所有可用麦克风的一部分即可实现相似的去回声性能。在本文中，我们提出使用流行的凸松弛方法，通过在预测滤波器系数上引入分组稀疏惩罚，对加权预测误差（WPE）多通道去回声算法进行麦克风子集选择。所得到的问题已被证明可以有效地使用加速近端梯度算法来解决。使用测量的脉冲响应进行的实验评估显示，所提出方法的性能接近通过穷举搜索获得的最优性能，无论是频率依赖性还是频率无关性麦克风子集选择。此外，使用少量麦克风进行频率无关性麦克风子集选择的性能仅略微劣于使用所有可用麦克风。
英文摘要：
Reverberation can severely degrade the quality of speech signals recorded using microphones in an enclosure. In acoustic sensor networks with spatially distributed microphones, a similar dereverberation performance may be achieved using only a subset of all available microphones. Using the popular convex relaxation method, in this paper we propose to perform microphone subset selection for the weighted prediction error (WPE) multi-channel dereverberation algorithm by introducing a group sparsity penalty on the prediction filter coefficients. The resulting problem is shown to be solved efficiently using the accelerated proximal gradient algorithm. Experimental evaluation using measured impulse responses shows that the performance of the proposed method is close to the optimal performance obtained by exhaustive search, both for frequency-dependent as well as frequency-independent microphone subset selection. Furthermore, the performance using only a few microphones for frequency-independent microphone subset selection is only marginally worse than using all available microphones.
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