Convolutive Underdetermined Source Separation through Weighted Interleaved ICA and Spatio-temporal Source Correlation
Convolutive Underdetermined Source Separation through Weighted Interleaved ICA and Spatio-temporal Source Correlation
复制标题
DOI：
10.1007/978-3-642-28551-6_28
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
F. Nesta;M. Omologo
中科院分区：
文献类型：
--
作者：
F. Nesta;M. Omologo
提出了一种新的卷积混合欠定声源分离方法。多个复值独立分量分析自适应联合估计混合矩阵和每个频率中多个源的时间活动。基于梯度的递归时间加权的结构强制每个伊卡自适应以估计与具有不相交时间活动的源相关的混合参数。通过对窄带分量进行多分辨率时空相关处理，减少了置换问题。最后，对齐的混合参数被用来恢复源通过L0范数最小化和基于单通道维纳滤波的后处理。在公开数据集上的实验结果表明，该方法是解决欠定源分离问题的有效方法。
This paper presents a novel method for underdetermined acoustic source separation of convolutive mixtures. Multiple complex-valued Independent Component Analysis adaptations jointly estimate the mixing matrix and the temporal activities of multiple sources in each frequency. A structure based on a recursive temporal weighting of the gradient enforces each ICA adaptation to estimate mixing parameters related to sources having a disjoint temporal activity. Permutation problem is reduced imposing a multiresolution spatio-temporal correlation of the narrow-band components. Finally, aligned mixing parameters are used to recover the sources throughL0-norm minimization and a post-processing based on a single channel Wiener filtering. Promising results obtained over a public dataset show that the proposed method is an effective solution to the underdetermined source separation problem.