Paper Info
Title
On Neural Blind Separation With Noise Suppression And Redundancy Reduction
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.
Year
DOI
Venue
1997
10.1142/S0129065797000239
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Field
DocType
Volume
Noise suppression,Pattern recognition,Computer science,Speech recognition,Unsupervised learning,Redundancy (engineering),Independent component analysis,Artificial intelligence,Artificial neural network,Blind signal separation,Adaptive learning,Machine learning
Journal
8
Issue
ISSN
Citations 
2
0129-0657
14
PageRank 
References 
Authors
1.61
0
4
Name
Order
Citations
PageRank
Juha Karhunen1863180.73
Andrzej Cichocki25228508.42
Wlodzimierz Kasprzak314921.04
P Pajunen429244.65