A Machine Learning Approach to Automatic Music Genre Classification

Silla Jr, Carlos N. and Koerich, A.L. and Kaestner, Celso A.A. (2008) A Machine Learning Approach to Automatic Music Genre Classification. Journal of the Brazilian Computer Society, 14 (3). pp. 7-18. ISSN 0104-6500. (Full text available)

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Abstract

This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). Music segments are also decomposed according to time segments obtained from the beginning, middle and end parts of the original music signal (time-decomposition). The final classification is obtained from the set of individual results, according to a combination procedure. Classical machine learning algorithms such as Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines and Multi- Layer Perceptron Neural Nets are employed. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,160 music pieces categorized in 10 musical genres. Experimental results show that the proposed ensemble approach produces better results than the ones obtained from global and individual segment classifiers in most cases. Some experiments related to feature selection were also conducted, using the genetic algorithm paradigm. They show that the most important features for the classification task vary according to their origin in the music signal.

Item Type: Article
Uncontrolled keywords: music genre classification, feature selection
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 12:11
Last Modified: 18 Jul 2012 08:52
Resource URI: http://kar.kent.ac.uk/id/eprint/24022 (The current URI for this page, for reference purposes)
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