Blind signal extraction of arbitrarily distributed, but temporally correlated signals - a neural network approach

Ruck Thawonmas, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1834-1840
Number of pages7
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE82-A
Issue number9
Publication statusPublished - 1999
Externally publishedYes

Keywords

  • Blind source separation and extraction
  • Neural networks
  • On-line adaptive algorithms

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