BLIND SPEECH SEPARATION USING HIGH ORDER STATISTICS

被引：0
|
作者：
Benabderrahmane, Y. [1 ]
Ben Salem, A. [2 ]
Selouani, S-A. [2 ]
O'Shaughnessy, D. [1 ]
机构：
[1] INRS EMT, 800 Gauchetiere O, Montreal, PQ H5A 1K6, Canada
[2] Univ Moncton, Moncton, NB E8S 1P6, Canada
来源：
2009 IEEE 22ND CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1 AND 2 | 2009年
关键词：
Blind source separation; instantaneous mixture; convolutive mixture; Independent Component Analysis; Higher Order Statistics; ALGORITHM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper deals with blind speech separation of instantaneous and convolutive mixtures of non-Gaussian sources. The separation criterion is based on higher order statistics (HOS) on the assumption that the sources are statistically independent. We propose to simplify and to improve the classical Herault-Jutten algorithm by choosing adequate high order non-linear functions for adaptation. The convolutive case is investigated through a model with impulse responses modeling the Head Related Transfer Function (HRTF). Experimental results show the efficiency of the proposed approach in terms of signal-to-interference ratio, when compared to the widely used fastICA algorithm. In the convolutive case a satisfactory separation of the sources has been achieved.
引用
收藏
页码：1136 / +
页数：2
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