Blind separation of filtered sources using state-space approach

Liqing Zhang, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In this paper we present a novel approach to multichannel blind separation/generalized deconvolution, assuming that both mixing and demixing models are described by stable linear state-space systems. We decompose the blind separation problem into two process: separation and state estimation. Based on the minimization of Kullback-Leibler Divergence, we develop a novel learning algorithm to train the matrices in the output equation. To estimate the state of the demixing model, we introduce a new concept, called hidden innovation, to numerically implement the Kalman filter. Computer simulations are given to show the validity and high effectiveness of the state-space approach.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PublisherNeural information processing systems foundation
Pages648-654
Number of pages7
ISBN (Print)0262112450, 9780262112451
Publication statusPublished - 1999
Externally publishedYes
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: 30 Nov 19985 Dec 1998

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Country/TerritoryUnited States
CityDenver, CO
Period30/11/985/12/98

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