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In this paper, we address the problem of blindly separating convolutive mixtures of spatially and temporally independent sources. Source densities are modelled as mixtures of Gaussians. We present an EM algorithm to compute Maximum Likelihood estimates of both the separating filters and the source density parameters, whereas in the state-of-the-art separating filters are usually estimated with gradient...
In this paper, we present a stochastic language-modeling tool which aims at retrieving variable-length phrases (multigrams), assuming n -gram dependencies between them, hence the name of the model: n -multigram. The estimation of the probability distribution of the phrases is intermixed with a phrase-clustering procedure in a way which jointly optimizes the likelihood of the data. As a result, the...
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