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Experimental evaluation of source separation with only one sensor

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dc.contributor.author Walker, Jacqueline
dc.contributor.author Bimbot, F.
dc.contributor.author Benaroya, L.
dc.date.accessioned 2018-02-26T09:58:44Z
dc.date.available 2018-02-26T09:58:44Z
dc.date.issued 2004
dc.identifier.uri http://hdl.handle.net/10344/6609
dc.description peer-reviewed en_US
dc.description.abstract We report on the evaluation of a new method for audio source separation using only one sensor. The method can be viewed as a generalization of Wiener filtering to locally stationary signals, where the sources are modelled using power spectral density dictionaries which are estimated during a training step. The experiments were designed to measure how separation performance varied with amount of training data, model complexity and the representativity of the training data. The results show that model complexity and training data representativity are more important than the amount of training data. en_US
dc.language.iso eng en_US
dc.relation.ispartof 6th IMA International Conference on Mathematics in Signal Processing en
dc.subject sensor en_US
dc.subject signals en_US
dc.title Experimental evaluation of source separation with only one sensor en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2018-02-26T09:55:16Z
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1396979
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


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