Optimization of a second-order statistics blind separation algorithm for Gaussian signals

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Abstract

The Second-Order Blind Identification (SOBI) algorithm (Belouchrani et al., 1997) is aimed at blind separation of static mixtures of stationary source signals with distinct spectra. It uses approximate joint diagonalization of empirical correlation matrices to estimate the mixing matrix. We show that SOBI's performance can be improved by transforming the joint diagonalization into a properly weighted nonlinear Least Squares problem. In the case of Gaussian sources, the optimal weights can be estimated consistently from the empirical correlation matrices. We demonstrate the substantial improvement by analysis and simulations.

Original languageEnglish
Article number7075536
JournalEuropean Signal Processing Conference
Volume2015-March
Issue numberMarch
StatePublished - 31 Mar 2000
Event2000 10th European Signal Processing Conference, EUSIPCO 2000 - Tampere, Finland
Duration: 4 Sep 20008 Sep 2000

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