Mixture Convolutive Independent Component Analysis
نویسندگان
چکیده
We propose a mixture model for blind source separation and deconvolution with adaptive source densities. Data is modelled as a multivariate locally linear random process. We derive an expression for the asymptotic likelihood of a linear process segment, which allows us to formulate and optimize a mixture model via the EM algorithm. The mixture model is able to represent nonstationary (locally, or piecewise stationary) signals. We exploit a convexity-based inequality to ensure monotonic increase of the likelihood with respect to the source density parameters. The model is applied to analysis of EEG signals.
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