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Classificacao Automatica de Generos Musicais Utilizando Metodos de Bagging e Boosting

By Carlos N. Silla Jr, Celso A.A. Kaestner and Alessandro L. Koerich

Abstract

This paper presents a study that uses meta-learning techniques to the task of automatic music genre classification. The meta-learning techniques we used are Bagging and Boosting. In both cases the component classifiers used in both approaches are Decision Trees, k-NN (k nearest neighbors) and Naive Bayes. The experiments were performed on a dataset containing 1,000 songs with 10 different genres. The achieved results show that the Bagging approach is promising while the Boosting approach seems to be inadequate to the problem

Topics: QA76
Year: 2005
OAI identifier: oai:kar.kent.ac.uk:24118

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