@CONFERENCE\{IMM2004-03315,
author = "R. K. Olsson and L. K. Hansen",
title = "Probabilistic blind deconvolution of non-stationary sources",
year = "2004",
month = "sep",
keywords = "Blind source separation, independent component analysis, speech processing",
pages = "1697-1700",
booktitle = "12th European Signal Processing Conference",
volume = "",
series = "",
editor = "",
publisher = "",
organization = "",
address = "",
url = "http://www2.imm.dtu.dk/pubdb/p.php?3315",
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."
}