Probabilistic blind deconvolution of non-stationary sources

Rasmus Kongsgaard Olsson, Lars Kai Hansen

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    Abstract

    We solve a class of blind signal separation problems using a constrained linear Gaussian model. The observed signal is modelled by a convolutive mixture of colored noise signals with additive white noise. We derive a time-domain EM algorithm `KaBSS' which estimates the source signals, the associated second-order statistics, the mixing filters and the observation noise covariance matrix. KaBSS invokes the Kalman smoother in the E-step to infer the posterior probability of the sources, and one-step lower bound optimization of the mixing filters and noise covariance in the M-step. In line with (Parra and Spence, 2000) the source signals are assumed time variant in order to constrain the solution sufficiently. Experimental results are shown for mixtures of speech signals.
    Original languageEnglish
    Title of host publication12th European Signal Processing Conference
    Publication date2004
    Pages1697-1700
    Publication statusPublished - 2004

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