@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.imm.dtu.dk/pubdb/p.php?3316",
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"
}