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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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@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}, }