Abstract
We propose in this paper a unique method to separate sources that may have different statistical properties, in the case of FIR convolutive mixtures. No constraint is necessary on the source statistics (i.i.d variables, Gaussian sources or temporally correlated sources..), nor on the number of each type of sources. On the contrary of previous works, no assumption of overdetermined mixtures is used. It relies on joint block-diagonalization of correlation matrices of some appropriate variables called differential complex signals, which are introduced in the paper.
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Servière, C. (2009). Separation of Convolutive Mixtures with Hybrid Sources. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_15
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DOI: https://doi.org/10.1007/978-3-642-00599-2_15
Publisher Name: Springer, Berlin, Heidelberg
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