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Predictability of music descriptor time series and its application to cover song detection

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dc.contributor.author Serrà Julià, Joan
dc.contributor.author Kantz, Holger
dc.contributor.author Serra, Xavier
dc.contributor.author Andrzejak, Ralph Gregor
dc.contributor.other Universitat Pompeu Fabra
dc.date.accessioned 2012-02-09T09:47:50Z
dc.date.available 2012-02-09T09:47:50Z
dc.date.issued 2011
dc.identifier.citation Serra J, Kantz H, Serra X, Andrzejak R G. Predictability of music descriptor time series and its application to cover song detection. IEEE Transactions on Audio, Speech and Language Processing. 2011; 20(2): 514-525. DOI 10.1109/TASL.2011.2162321
dc.identifier.issn 1558-7916
dc.identifier.uri http://hdl.handle.net/10230/16207
dc.description.abstract Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance /nbut can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.
dc.description.sponsorship This work has been partially funded by the Deutscher Akademischer Austausch Dienst (A/09/96235), by the projects Classical Planet (MITYC: TSI-070100- 2009-407) and DRIMS (MICINN: TIN2009-14247-C02-01), by the Spanish Ministry of Education /nand Science (BFU2007-61710) and by the Max Planck Institute for the Physics of Complex Systems.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof IEEE Transactions on Audio, Speech and Language Processing. 2011;20(2):514-25
dc.rights © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works./nThe final published article can be found at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5955095&tag=1
dc.subject.other Sèries temporals -- Anàlisi
dc.subject.other Música -- Informàtica
dc.subject.other So -- Tractament per ordinador
dc.title Predictability of music descriptor time series and its application to cover song detection
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1109/TASL.2011.2162321
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TIN2009-14247
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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