On neural blind separation with noise suppression and redundancy reduction.
- 1 April 1997
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Neural Systems
- Vol. 8 (2) , 219-237
- https://doi.org/10.1142/s0129065797000239
Abstract
Noise is an unavoidable factor in real sensor signals. We study how additive and convolutive noise can be reduced or even eliminated in the blind source separation (BSS) problem. Particular attention is paid to cases in which the number of sensors is larger than the number of sources. We propose various methods and associated adaptive learning algorithms for such an extended BSS problem. Performance and validity of the proposed approaches are demonstrated by extensive computer simulations.Keywords
This publication has 2 references indexed in Scilit:
- Robust learning algorithmfor blind separation of signalsElectronics Letters, 1994
- Robust estimation of principal components by using neural network learning algorithmsElectronics Letters, 1993