@CONFERENCE\{IMM2004-03316,
    author       = "R. K. Olsson and L. K. Hansen",
    title        = "Estimating the number of sources in a noisy convolutive mixture using {BIC}",
    year         = "2004",
    month        = "sep",
    keywords     = "Blind source separation, independent component analysis, speech processing, Bayes information criterion",
    pages        = "618-625",
    booktitle    = "5th International Conference on Independent Component Analysis and Blind Signal Separation",
    volume       = "",
    series       = "",
    editor       = "C. G. Puntonet, A. Prieto",
    publisher    = "Springer Berlin",
    organization = "",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3316-full.html",
    abstract     = "The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the sources. The algorithm, known as ‘KaBSS’, employs a Gaussian linear model for the mixture, i.e. {AR} models for the sources, linear mixing filters and a white Gaussian noise model. Using an {EM} algorithm, which invokes the Kalman smoother in the {E-}step, all model parameters are estimated and the exact posterior probability of the sources conditioned on the observations is obtained. The log-likelihood of the parameters is computed exactly in the process, which allows for model evidence comparison assisted by the {BIC} approximation. This is used to determine the activity pattern of two speakers in a convolutive mixture of speech signals.",
    isbn_issn    = "3-540-23056-4"
}