These algorithms generalize independent component analysis to the case of convolutive mixtures and exhibit superior performance on instantaneous mixtures. Different rules are obtained by learning generative models in the frequency and time domains, whereas a hybrid frequency-time model leads to the best performance. The resulting learning rules achieve separation by exploiting high-order spatiotemporal statistics of the mixture data. Our approach is based on formulating the separation problem as a learning task of a spatiotemporal generative model, whose parameters are adapted iteratively to minimize suitable error functions, thus ensuring stability of the algorithms. We derive a novel family of unsupervised learning algorithms for blind separation of mixed and convolved sources.
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