Classificacao Automatica de Generos Musicais Utilizando Metodos de Bagging e Boosting

Silla Jr, Carlos N. and Kaestner, Celso A.A. and Koerich, Alessandro L. (2005) Classificacao Automatica de Generos Musicais Utilizando Metodos de Bagging e Boosting. In: 10th Brazilian Symposium on Computer Music. (Full text available)

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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.

Item Type: Conference or workshop item (Paper)
Additional information: Paper in Brazilian Portuguese.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:16
Last Modified: 11 Jul 2014 11:16
Resource URI: http://kar.kent.ac.uk/id/eprint/24118 (The current URI for this page, for reference purposes)
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