A parametric density model for blind source separation

Mingjun Zhong, Junfu Du

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)199-207
Number of pages9
JournalNeural Processing Letters
Issue number3
Publication statusPublished - Jun 2007

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