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.
authors.affiliation: Helsinki University of Technology, Rakentajanaukio, Finland. Juha.Karhunen@hut.fi
authors.forename: J
authors.initials: J
authors.lastname: Karhunen
authors.forename: A
authors.initials: A
authors.lastname: Cichocki
authors.forename: W
authors.initials: W
authors.lastname: Kasprzak
authors.forename: P
authors.initials: P
authors.lastname: Pajunen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted