School of Computing

Classificacao automatica de generos musicais utilizando metodos de bagging e boosting

Carlos Nascimento Silla Jr., Celso Antonio Alves Kaestner, and Alessandro Lameiras Koerich

In 10th Brazilian Symposium on Computer Music, pages 182-196, September 2005 Paper in Brazilian Portuguese.

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.

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Bibtex Record

@inproceedings{2929,
author = {Silla Jr., Carlos Nascimento and Kaestner, Celso Antonio Alves and Koerich, Alessandro Lameiras},
title = {Classificacao Automatica de Generos Musicais Utilizando Metodos de Bagging e Boosting},
month = {September},
year = {2005},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {Paper in Brazilian Portuguese.},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2005/2929},
    publication_type = {inproceedings},
    submission_id = {1156_1245728595},
    refereed = {yes},
    booktitle = {10th Brazilian Symposium on Computer Music},
}

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