A maximum a posteriori estimate for the source separation problem with statistical knowledge about the mixing matrix

Authors:
Jorge Igual;Andres Camacho;Pablo Bernabeu;Luis Vergara
Affiliations:
EPSA-Pza Ferrandiz y Carbonell, Universidad Politecnica Valencia Comunicaciones, no. 1, 03801 Alcoy (Alicante), Spain;EPSA-Pza Ferrandiz y Carbonell, Universidad Politecnica Valencia Comunicaciones, no. 1, 03801 Alcoy (Alicante), Spain;EPSA-Pza Ferrandiz y Carbonell, Universidad Politecnica Valencia Comunicaciones, no. 1, 03801 Alcoy (Alicante), Spain;ETSIT-Camino de Vera s/n, 46023 Valencia, Spain
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

In the blind source separation (BSS) problem, nothing is supposed about the mixing matrix entries. When a prior knowledge about their values is available, the BSS algorithms can be optimized considering this information. We obtain the maximum a posteriori estimate of the source separation problem for prewhitened observed signals and prior statistical knowledge about the mixing matrix for the real, linear, instantaneous case. As new information is included in the formulation of the problem, the variance of classical BSS algorithms can be reduced.