Pattern recognition approach for music style identification using shallow statistical descriptors
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Campo DC | Valor | Idioma |
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dc.contributor | Reconocimiento de Formas e Inteligencia Artificial | en |
dc.contributor.author | Ponce de León Amador, Pedro José | - |
dc.contributor.author | Iñesta, José M. | - |
dc.contributor.other | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | en |
dc.date.accessioned | 2009-02-19T11:23:39Z | - |
dc.date.available | 2009-02-19T11:23:39Z | - |
dc.date.created | 2004 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.citation | PONCE DE LEÓN AMADOR, Pedro; IÑESTA QUEREDA, José Manuel. "Pattern recognition approach for music style identification using shallow statistical descriptors". IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. Vol. 37, No. 2 (March 2007). ISSN 1094-6977, pp. 248-257 | en |
dc.identifier.issn | 1094-6977 (Print) | - |
dc.identifier.issn | 1558-2442 (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/9687 | - |
dc.description.abstract | In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval applications. One challenging task in this area is the automatic recognition of musical style, having a number of applications like indexing and selecting musical databases. From melodies symbolically represented as digital scores (standard musical instrument digital interface files), a number of melodic, harmonic, and rhythmic statistical descriptors are computed and their classification capability assessed in order to build effective description models. A framework for experimenting in this problem is presented, covering the feature extraction, feature selection, and classification stages, in such a way that new features and new musical styles can be easily incorporated and tested. Different classification methods, like Bayesian classifier, nearest neighbors, and self-organizing maps, are applied. The performance of such algorithms against different description models and parameters is analyzed for two particular musical styles, jazz and classical, used as an initial benchmark for our system. | en |
dc.description.sponsorship | This work was supported by the projects: Generalitat Valenciana GV043-541 and Spanish CICyT TIC2003–08496–C04, partially supported by EU ERDF. | en |
dc.language | eng | en |
dc.publisher | IEEE Systems, Man, and Cybernetics Society | en |
dc.subject | Bayesian classifier | en |
dc.subject | Music style classification | en |
dc.subject | Nearest neighbors | en |
dc.subject | Self-organizing maps (SOMs) | en |
dc.subject.other | Lenguajes y Sistemas Informáticos | en |
dc.title | Pattern recognition approach for music style identification using shallow statistical descriptors | en |
dc.type | info:eu-repo/semantics/article | en |
dc.peerreviewed | si | en |
dc.identifier.doi | 10.1109/TSMCC.2006.876045 | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas INV - BAES - Artículos de Revistas |
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