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.
|Number of pages||7|
|Journal||IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences|
|Publication status||Published - 1999|
- Blind source separation and extraction
- Neural networks
- On-line adaptive algorithms