Model-based cover song detection via threshold autoregressive forecasts

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• dc.contributor.author Serrà Julià, Joan
• dc.contributor.author Kantz, Holger
• dc.contributor.author Andrzejak, Ralph Gregor
• dc.date.accessioned 2021-01-29T07:31:02Z
• dc.date.available 2021-01-29T07:31:02Z
• dc.date.issued 2010
• dc.description Comunicació presentada a ACM Multimedia, International Workshop on Machine Learning and Music (MML), celebrat del 25 al 29 d'octubre de 2010 a Florència, Itàlia.
• dc.description.abstract Current systems for cover song detection are based on a model-free approach: they basically search for similarities in descriptor time series reflecting the evolution of tonal information in a musical piece. In this contribution we propose the use of a model-based approach. In particular, we explore threshold autoregressive models and the concept of cross-prediction error, i.e. a measure of to which extent a model trained on one song's descriptor time series is able to predict the covers'. Results indicate that the considered approach can provide competitive accuracies while being considerably fast and with potentially less storage requirements. Furthermore, the approach is parameter-free from the user's perspective, what provides a robust and straightforward application of it.en
• dc.description.sponsorship We thank Emilia G´omez for her review on a previous version of this article. J.S. has been partially funded by the A/09/96235 grant from the Deutscher Akademisch Austausch Dienst and by the Music 3.0 (TSI-070100- 2008-318) and Buscamedia (CEN-20091026) projects. R.G.A. has been funded by the BFU2007-61710 grant of the Spanish Ministry of Education and Science.
• dc.format.mimetype application/pdf
• dc.identifier.citation Serrà J, Kantz H, Andrzejak RG. Model-based cover song detection via threshold autoregressive forecasts. In: ACM Multimedia, International Workshop on Machine Learning and Music (MML); 2010 Oct 25-29; Firenze, Italy. New York: Association for Computing Machinery; 2010. p. 13-6. DOI: 10.1145/1878003.1878008
• dc.identifier.doi http://dx.doi.org/10.1145/1878003.1878008
• dc.identifier.uri http://hdl.handle.net/10230/46290
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof ACM Multimedia, International Workshop on Machine Learning and Music (MML); 2010 Oct 25-29; Firenze, Italy. New York: Association for Computing Machinery; 2010. p. 13-6
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710
• dc.rights © ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Multimedia, International Workshop on Machine Learning and Music (MML), (2011) http://doi.acm.org/10.1145/1878003.1878008
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Musicen
• dc.subject.keyword Information retrievalen
• dc.subject.keyword Threshold autoregressive modelsen
• dc.subject.keyword Predictionen
• dc.subject.keyword Cover songsen
• dc.subject.keyword Versionsen
• dc.title Model-based cover song detection via threshold autoregressive forecastsen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion