Blind separation of convolutive mixtures by decorrelation
Signal Processing
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Blind separation of underwater acoustic signals
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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We present a new method for separating non-stationary sources from their convolutive mixtures based on approximate joint diagonalization of the observed signals' cross-spectral density matrices. Several blind source separation (BSS) algorithms have been proposed which use approximate joint diagonalization of a set of scalar matrices to estimate the instantaneous mixing matrix. We extend the concept of approximate joint diagonalization to estimate MIMO FIR channels. Based on this estimate we then design a separating network which will recover the original sources up to only a permutation and scaling ambiguity for minimum phase channels. We eliminate the commonly experienced problem of arbitrary scaling and permutation at each frequency bin, by optimizing the cost function directly with respect to the time-domain channel variables. We demonstrate the performance of the algorithm by computer simulations using real speech data.