Blind Source Separation Based on K-SCA Assumption

摘要：

The blind source.separation (BSS) based on K-SCA is discussed in this paper. The first challenging task of this approach is how to estimate the unknown mixing matrix precisely, to solve this problem, the algorithm based on hyperplane membership function is proposed. In contrast to the classical methods, the required key condition on sparsity of the sources can be considerably relaxed, and the algorithm has a good ability of anti-noise. Several experiments involving speech signals show the effectiveness and efficiency of this method.

关键词： underdetermined blind source separation hyperplane membership function sparse analysis

作者: Wen Yang Hongyi Zhang

作者单位: College of Electronics & Information Engineering Henan University of Science and Technology Luoyang, Department of Electronic and Electric Engineering XIA MEN university of technology XiaMen, Fujian 36

会议类型: 国际会议

会议名称: 2010 3rd IEEE International Conference on Computer Science and Information Technology(第三届IEEE计算机科学与信息技术国际会议 ICCSIT 2010)

会议地点: 成都

会议语种:英文

页码: 116-121

在线出版日期: 2010-07-07（万方平台首次上网日期，不代表论文的发表时间）