PROBABILISTIC BLIND DECONVOLUTION OF NON-STATIONARY SOURCES (ThuPmPO2)

Author(s) : Rasmus Kongsgaard Olsson (Informatics and Mathematical Modelling, Technical University of Denmark, Denmark) Lars Kai Hansen (Informatics and Mathematical Modelling, Technical University of Denmark, Denmark)

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.

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