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dc.contributor.authorVincent, Een_US
dc.contributor.authorJafari, MGen_US
dc.contributor.authorAbdallah, SAen_US
dc.contributor.authorPlumbley, MDen_US
dc.contributor.authorDavies, MEen_US
dc.contributor.editorWang, Wen_US
dc.date.accessioned2014-01-21T12:18:52Z
dc.date.issued2011en_US
dc.identifier.isbn1615209204en_US
dc.identifier.isbn9781615209194en_US
dc.identifier.other7
dc.identifier.other7
dc.identifier.other7en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/5265
dc.descriptionThis is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007
dc.descriptionfile: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04
dc.descriptionfile: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04en_US
dc.description.abstractMost sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.en_US
dc.format.extent162 - 185 (23)en_US
dc.publisherIGI Globalen_US
dc.relation.ispartofMachine Audition: Principles, Algorithms and Systemsen_US
dc.titleProbabilistic Modeling Paradigms for Audio Source Separationen_US
dc.typeBook chapter
dc.identifier.doi10.4018/978-1-61520-919-4.ch007en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.publisher-urlhttp://www.igi-global.com/book/machine-audition-principles-algorithms-systems/40288en_US


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