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EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL

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Titel: EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL
Autoren: Makinen, Toni, Kiranyaz, Serkan, Pulkkinen, Jenni, Gabbouj, Moncef
Publikationsjahr: 2012
Bestand: The Hong Kong University of Science and Technology: HKUST Institutional Repository
Schlagwörter: Feature generation, Particle swarm optimization, Neural networks, Content-based classification
Beschreibung: Many commonly applied audio features suffer from certain limitations in describing the data content for classification and retrieval purposes. To remedy this drawback, in this paper we propose an evolutionary feature synthesis (EFS) technique, which is applied over traditional audio features to improve their data discrimination power. The underlying evolutionary optimization algorithm performs both feature selection and feature generation in an interleaved manner, optimizing also the dimensionality of the synthesized feature vector. The process is based on multi-dimensional particle swarm optimization (MD PSO) with two additional techniques: the fractional global best formation (FGBF) and simulated annealing (SA). The experimented classification and retrieval performances over a 16-class audio database show improvements of up to 11% when compared to the corresponding performances of the original features.
Publikationsart: conference object
Sprache: English
Relation: http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000310623800296
Verfügbarkeit: http://repository.hkust.edu.hk/ir/Record/1783.1-53465
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Dokumentencode: edsbas.BCD92E31
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  Label: Title
  Group: Ti
  Data: EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Makinen%2C+Toni%22">Makinen, Toni</searchLink><br /><searchLink fieldCode="AR" term="%22Kiranyaz%2C+Serkan%22">Kiranyaz, Serkan</searchLink><br /><searchLink fieldCode="AR" term="%22Pulkkinen%2C+Jenni%22">Pulkkinen, Jenni</searchLink><br /><searchLink fieldCode="AR" term="%22Gabbouj%2C+Moncef%22">Gabbouj, Moncef</searchLink>
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  Label: Publication Year
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  Data: 2012
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  Data: The Hong Kong University of Science and Technology: HKUST Institutional Repository
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  Data: <searchLink fieldCode="DE" term="%22Feature+generation%22">Feature generation</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+networks%22">Neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Content-based+classification%22">Content-based classification</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Many commonly applied audio features suffer from certain limitations in describing the data content for classification and retrieval purposes. To remedy this drawback, in this paper we propose an evolutionary feature synthesis (EFS) technique, which is applied over traditional audio features to improve their data discrimination power. The underlying evolutionary optimization algorithm performs both feature selection and feature generation in an interleaved manner, optimizing also the dimensionality of the synthesized feature vector. The process is based on multi-dimensional particle swarm optimization (MD PSO) with two additional techniques: the fractional global best formation (FGBF) and simulated annealing (SA). The experimented classification and retrieval performances over a 16-class audio database show improvements of up to 11% when compared to the corresponding performances of the original features.
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RecordInfo BibRecord:
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    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Feature generation
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Neural networks
        Type: general
      – SubjectFull: Content-based classification
        Type: general
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      – TitleFull: EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL
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            NameFull: Makinen, Toni
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            NameFull: Kiranyaz, Serkan
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            NameFull: Pulkkinen, Jenni
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            NameFull: Gabbouj, Moncef
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            – D: 01
              M: 01
              Type: published
              Y: 2012
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