A parametric density model for blind source separation |
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Authors: | Mingjun Zhong Junfu Du |
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Affiliation: | (1) Department of Applied Mathematics, Dalian Nationalities University, Dalian, 116600, P. R. China;(2) Science Institute, Dalian Fisheries University, Dalian, 116023, P. R. China |
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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. |
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Keywords: | Blind source separation Independent component analysis EM algorithm Variational method |
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