On neural blind separation with noise suppression and redundancy reduction.

J. Karhunen, A. Cichocki, W. Kasprzak, P. Pajunen

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)219-237
Number of pages19
JournalInternational Journal of Neural Systems
Issue number2
Publication statusPublished - Apr 1997
Externally publishedYes


Dive into the research topics of 'On neural blind separation with noise suppression and redundancy reduction.'. Together they form a unique fingerprint.

Cite this