A parametric density model for blind source separation

被引：0
作者：
Mingjun Zhong
Junfu Du
机构：
[1] Dalian Nationalities University,Department of Applied Mathematics
[2] Dalian Fisheries University,Science Institute
来源：
Neural Processing Letters | 2007年 / 25卷
关键词：
Blind source separation; Independent component analysis; EM algorithm; Variational method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a parametric mixture density model is employed to be the source prior in blind source separation (BSS). A strict lower bound on the source prior is derived by using a variational method, which naturally enables the intractable posterior to be represented as a gaussian form. An expectation-maximization (EM) algorithm in closed form is therefore derived for estimating the mixing matrix and inferring the sources. Simulation results show that the proposed variational expectation-maximization algorithm can perform blind separation of not only speech source of more sources than mixtures, but also binary source of more sources than mixtures.
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页码：199 / 207
页数：8
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