A parametric density model for blind source separation

Authors:
Mingjun Zhong;Junfu Du
Affiliations:
Department of Applied Mathematics, Dalian Nationalities University, Dalian, P. R. China 116600;Science Institute, Dalian Fisheries University, Dalian, P. R. China 116023
Venue:
Neural Processing Letters
Year:
2007

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Abstract

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.