@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.compute.dtu.dk/pubdb/pubs/3315-full.html",
    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."
}