Estimating the number of sources in a noisy convolutive mixture using BIC

Rasmus Kongsgaard Olsson, Lars Kai Hansen

AbstractThe 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.
KeywordsBlind source separation, independent component analysis, speech processing, Bayes information criterion
TypeConference paper [With referee]
Conference5th International Conference on Independent Component Analysis and Blind Signal Separation
EditorsC. G. Puntonet, A. Prieto
Year2004    Month September    pp. 618-625
PublisherSpringer Berlin
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
ISBN / ISSN3-540-23056-4
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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