SPARSITY AND LOW-RANK AMPLITUDE BASED BLIND SOURCE SEPARATION

被引：0
|
作者：
Feng, Fangchen [1 ]
Kowalski, Matthieu [1 ]
机构：
[1] Univ Paris Saclay, Univ Paris Sud, L2S, CNRS,CENT SUPELEC, Gif Sur Yvette, France
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年
关键词：
Blind source separation; sparsity; low-rank; multichannel non-negative matrix factorization; NONNEGATIVE MATRIX FACTORIZATION; AUDIO SOURCE SEPARATION; MIXTURES;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
This paper presents a new method for blind source separation problem in reverberant environments with more sources than microphones. Based on the sparsity property in the time-frequency domain and the low-rank assumption of the spectrogram of the source, the STRAUSS (SparsiTy and low-Rank AmplitUde based Source Separation) method is developed. Numerical evaluations show that the proposed method outperforms the existing multichannel NMF approaches, while it is exclusively based on amplitude information.
引用
收藏
页码：571 / 575
页数：5
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