Convolutive blind source separation in the frequency domain based on sparse representation

被引:65
作者
He, Zhaoshui [1 ]
Xie, Shengli
Ding, Shuxue
Cichocki, Andrzej
机构
[1] S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China
[2] RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Saitama 3510198, Japan
[3] Univ Aizu, Sch Comp Sci & Engn, Fukushima 9658580, Japan
[4] Polish Acad Sci, Syst Res Inst, PL-00901 Warsaw, Poland
[5] Warsaw Univ Technol, PL-00661 Warsaw, Poland
来源
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2007年 / 15卷 / 05期
基金
中国国家自然科学基金;
关键词
complex Laplacian-like distribution; convolutive blind source separation (CBSS); frequency domain; permutation problem; probability density function; sparse representation (SR);
D O I
10.1109/TASL.2007.898457
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Convolutive blind source separation (CBSS) that exploits the sparsity of source signals in the frequency domain is addressed in this paper. We assume the sources follow complex Laplacian-like distribution for complex random variable, in which the real part and imaginary part of complex-valued source signals are not necessarily independent. Based on the maximum a posteriori (MAP) criterion, we propose a novel natural gradient method for complex sparse representation. Moreover, a new CBSS method is further developed based on complex sparse representation. The developed CBSS algorithm works in the frequency domain. Here, we assume that the source signals are sufficiently sparse in the frequency domain. If the sources are sufficiently sparse in the frequency domain and the filter length of mixing channels is relatively small and can be estimated, we can even achieve underdetermined CBSS. We illustrate the validity and performance of the proposed learning algorithm by several simulation examples.
引用
收藏
页码:1551 / 1563
页数:13
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