Probabilistic blind deconvolution of non-stationary sources

Rasmus Kongsgaard Olsson, Lars Kai Hansen

AbstractWe 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.
KeywordsBlind source separation, independent component analysis, speech processing
TypeConference paper [With referee]
Conference12th European Signal Processing Conference
Year2004    Month September    pp. 1697-1700
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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