dc.creatorBarbedo, JGA
dc.creatorLopes, A
dc.date2007
dc.date2014-11-17T07:02:50Z
dc.date2015-11-26T16:40:22Z
dc.date2014-11-17T07:02:50Z
dc.date2015-11-26T16:40:22Z
dc.date.accessioned2018-03-28T23:24:16Z
dc.date.available2018-03-28T23:24:16Z
dc.identifierEurasip Journal On Advances In Signal Processing. Springer International Publishing Ag, 2007.
dc.identifier1687-6172
dc.identifierWOS:000247961300001
dc.identifier10.1155/2007/64960
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/55215
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/55215
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/55215
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1272690
dc.descriptionWe present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.
dc.languageen
dc.publisherSpringer International Publishing Ag
dc.publisherCham
dc.publisherSuíça
dc.relationEurasip Journal On Advances In Signal Processing
dc.relationEURASIP J. Adv. Signal Process.
dc.rightsaberto
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.titleAutomatic genre classification of musical signals
dc.typeArtículos de revistas


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