Abstract

In this paper, we analyze and extend a class of adaptive networks for secondorder blind decorrelation of instantaneous signal mixtures. Firstly, we compare the performance of the decorrelation neural network employing global knowledge of the adaptive coefficients with a similar structure whose coefficients are adapted via local output connections. Through statistical analyses, the convergence behaviors and stability bounds for the algorithms' step sizes are studied and derived. Secondly, we analyze the behaviors of locally-adaptive multilayer decorrelation networks and quantify their performances for poorly-conditioned signal mixtures. Thirdly, we derive a robust locally-adaptive network structure based on a posteriori output signals that remains stable for any step size value. Finally, we present an extension of the locally-adaptive network for linear-phase temporal and spatial whitening of multichannel signals. Simulations verify the analyses and indicate the usefulness of the locall...

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