Nonnegative Matrix Factorization with Disjointness Constraints for Single Channel Speech Separation

摘要：

　　This paper addresses the problem of single channel speech separation using nonnegative matrix factorization (NMF) technique.In general,the standard NMF algorithm by itself does not guarantee statistical relationship between the matrices it computes.This leads to poor separation performance.To solve this problem,we propose to enforce disjointness constraint on the standard NMF algorithm in the separation process.The multiplicative update rules of the proposed algorithm are also derived in this paper.The performance of the proposed method is compared with standard NMF algorithm,which is based on the same linear model.The experimental results show that the proposed method achieves a better separation quality than the standard NMF.

关键词： Single channel speech separation Nonnegative matrix factorization Dictionary learning Blind source separation

作者: Jianjun Huang Xiongwei Zhang Yafei Zhang Haijia Wu

作者单位: Institute of Command Automation, PLA University of Science and Technology,Nanjing 210007, China

会议类型: 国际会议

会议名称: 2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))

会议地点: 成都

会议语种:英文

页码: 1241-1245

在线出版日期: 2012-11-09（万方平台首次上网日期，不代表论文的发表时间）