Learning melodic analysis rules

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10045/20836
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dc.contributorReconocimiento de Formas e Inteligencia Artificialen
dc.contributor.authorIllescas, Plácido R.-
dc.contributor.authorRizo, David-
dc.contributor.authorIñesta, José M.-
dc.contributor.authorRamírez, Rafael-
dc.contributor.otherUniversidad de Alicante. Departamento de Lenguajes y Sistemas Informáticosen
dc.date.accessioned2012-02-27T16:45:53Z-
dc.date.available2012-02-27T16:45:53Z-
dc.date.issued2011-12-
dc.identifier.urihttp://hdl.handle.net/10045/20836-
dc.descriptionComunicación presentada en MML 2011, 4th International Workshop on Machine Learning and Music: Learning from Musical Structure, Sierra Nevada, Spain, December 17, 2011.en
dc.description.abstractAutomatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules.en
dc.languageengen
dc.subjectAutomatic musical analysisen
dc.subjectAutomatic melodic analysisen
dc.subjectMelodic analysis rulesen
dc.subject.otherLenguajes y Sistemas Informáticosen
dc.titleLearning melodic analysis rulesen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.peerreviewedsien
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
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