The increasing amount of music data approaching the scale of ten million of tracks poses the challenge of organizing such huge information. Audio Tag Classification is a sub-area in Music Information Retrieval. Its objective is to predict human motivated tags given the acoustic data. One major problem in this procedure is the training of the classifier. An important step in the training is the selection of the appropriate acoustical features. This paper explores two selection approaches: greedy and spitting. Experimental results indicate that the proposed spitting algorithm has a superior performance both in classification (F-measure score) and speed (lower computational requirements).
Información general
Fecha de exposición:agosto 2011
Fecha de publicación:2011
Idioma del documento:Inglés
Evento:XII Argentine Symposium on Technology (AST 2011) (XL JAIIO, Córdoba, 29 de agosto al 2 de septiembre de 2011)
Institución de origen:Sociedad Argentina de Informática e Investigación Operativa
Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)