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Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Signals : A Neural Network Approach (Special Section on Nonlinear Theory and Its Applications)

In this paper, we discuss a neural network approach for blind signal extraction of temporally correlated sources. Assuming autoregressive models of source signals, we propose a very simple neural network model and an efficient online adaptive algorithm that extract, from linear mixtures, a temporally correlated source with an arbitrary distribution, including a colored Gaussian source and a source with extremely low value (or even zero) of kurtosis. We then combine these extraction processing units with deflation processing units to extract such sources sequentially in a cascade fashion. Theory and simulations show that the proposed neural network successfully extracts all arbitrarily distributed, but temporally correlated source signals from linear mixtures. key words: blind source separation and extraction, neural networks, on-line adaptive algorithms