Time-Frequency Approach to Underdetermined Blind Source Separation

被引：100
|
作者：
Xie, Shengli [1 ,2 ]
Yang, Liu [1 ]
Yang, Jun-Mei [1 ]
Zhou, Guoxu [1 ]
Xiang, Yong [3 ]
机构：
[1] S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China
[2] Guangdong Univ Technol, Fac Automat, Guangzhou 510006, Guangdong, Peoples R China
[3] Deakin Univ, Sch Informat Technol, Melbourne, Vic 3125, Australia
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 02期
基金：
澳大利亚研究理事会; 中国国家自然科学基金;
关键词：
Khatri-Rao product; underdetermined blind source separation; Wigner-Ville distribution; IDENTIFICATION; MIXTURES; ALGORITHMS;
D O I：
10.1109/TNNLS.2011.2177475
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new time-frequency (TF) underdetermined blind source separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary sources from M(M < N) mixtures. First, an improved method is proposed for estimating the mixing matrix, where the negative value of the auto WVD of the sources is fully considered. Then after extracting all the auto-term TF points, the auto WVD value of the sources at every auto-term TF point can be found out exactly with the proposed approach no matter how many active sources there are as long as N <= 2M - 1. Further discussion about the extraction of auto-term TF points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones.
引用
收藏
页码：306 / 316
页数：11
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