Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

Silla Jr, Carlos N. and Kaestner, Celso A.A. and Koerich, Alessandro L. (2006) Automatic Genre Classification of Latin Music Using Ensemble of Classifiers. In: Anais do XXVI Congresso da Sociedade Brasileira de Computação - XXXIII Seminário Integrado de Software e Hardware. (Full text available)

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Abstract

This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy. Experiments carried out on a dataset containing 600 music samples from two Latin genres (Tango and Salsa) have shown that for the task of automatic music genre classification, the features extracted from the middle and end music segments provide better results than using the beginning music segment. Furthermore, the proposed ensemble method provides better accuracy than using single classifiers and any individual segment.

Item Type: Conference or workshop item (Paper)
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 12:14
Last Modified: 11 Jul 2014 11:17
Resource URI: http://kar.kent.ac.uk/id/eprint/24094 (The current URI for this page, for reference purposes)
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