Superimposed event detection by particle filters

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage668en_US
dc.citation.issueNumber7en_US
dc.citation.spage662en_US
dc.citation.volumeNumber5en_US
dc.contributor.authorUrfalioglu, O.en_US
dc.contributor.authorKuruoglu, E. E.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.date.accessioned2015-07-28T11:59:50Z
dc.date.available2015-07-28T11:59:50Z
dc.date.issued2011en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn this study, the authors consider online detection and separation of superimposed events by applying particle filtering. They observe only a single-channel superimposed signal, which consists of a background signal and one or more event signals in the discrete-time domain. It is assumed that the signals are statistically independent and can be described by random processes with known parametric models. The activation and deactivation times of event signals are assumed to be unknown. This problem can be described as a jump Markov system (JMS) in which all signals are estimated simultaneously. In a JMS, states contain additional parameters to identify models. However, for superimposed event detection, the authors show that the underlying JMS-based particle-filtering method can be reduced to a standard Markov chain method without additional parameters. Numerical experiments using real-world sound processing data demonstrate the effectiveness of their approach.en_US
dc.identifier.doi10.1049/iet-spr.2010.0022en_US
dc.identifier.issn1751-9675
dc.identifier.urihttp://hdl.handle.net/11693/12047
dc.language.isoEnglishen_US
dc.publisherThe Institution of Engineering and Technologyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1049/iet-spr.2010.0022en_US
dc.source.titleIET Signal Processingen_US
dc.subjectSource separationen_US
dc.subjectSystemsen_US
dc.titleSuperimposed event detection by particle filtersen_US
dc.typeArticleen_US

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