Underdetermined Blind Source Separation Based on Fuzzy C-Means Clustering and Sparse Representation

摘要：

Traditional blind source separation is based on over-determined, but the underdetermined is more consistent with actual situation, based on sparse representation, Bofill proposed two step method to solve the problem under some assumptions. The accuracy of the mixture affects the recovery of sources, avoiding the subjectivity of choosing parameter, using the fuzzy C-means clustering to get the mixing matrix estimation; at the same time, to lessen the requirement of sparsity, combining ICA with SCA, based on the criterion of negentropy, sources can be separated. The test shows that the algorithm proposed here get a good result

关键词： blind source separation underdetermined sparse separation fuzzy C-means clustering

作者: Chaozhu Zhang Cui Zheng

作者单位: Information and Communication Department Harbin Engineering University Harbin, China

会议类型: 国际会议

会议名称: 2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)

会议地点: 长沙

会议语种:英文

页码: 445-449

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）