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dc.contributor.authorEverson, Richard M.en_GB
dc.contributor.authorRoberts, Stephenen_GB
dc.date.accessioned2012-04-25T15:59:46Zen_GB
dc.date.accessioned2013-03-20T12:10:07Z
dc.date.issued2000en_GB
dc.description.abstractBlind source separation attempts to recover independent sources which have been linearly mixed to produce observations. We consider blind source separation with non-stationary mixing, but stationary sources. The linear mixing of the independent sources is modelled as evolving according to a Markov process, and a method for tracking the mixing and simultaneously inferring the sources is presented. Observational noise is included in the model. The technique may be used for online filtering or retrospective smoothing. The tracking of mixtures of temporally correlated is examined and sampling from within a sliding window is shown to be effective for destroying temporal correlations. The method is illustrated with numerical examples.en_GB
dc.identifier.citation26 (1-2), pp. 15 - 23en_GB
dc.identifier.urihttp://hdl.handle.net/10036/3511en_GB
dc.language.isoenen_GB
dc.relation.urlhttp://dx.doi.org/10.1023/A:1008183014430en_GB
dc.titleBlind source separation for non-stationary mixingen_GB
dc.typeArticleen_GB
dc.date.available2012-04-25T15:59:46Zen_GB
dc.date.available2013-03-20T12:10:07Z
dc.identifier.issn0922-5773en_GB
dc.descriptionThe original publication is available at www.springerlink.comen_GB
dc.identifier.journalJournal of VLSI Signal Processingen_GB


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