Time-Space Ensemble Strategies for Automatic Music Genre Classification

Silla Jr, Carlos N. and Kaestner, Celso A.A. and Koerich, Alessandro L. (2006) Time-Space Ensemble Strategies for Automatic Music Genre Classification. In: Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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In this paper we propose a novel timespace ensemblebased approach for the task of automatic music genre classification. Ensemble strategies employ several classifiers to different views of the problem space, and combination rules in order to produce the final classification decision. In our approach we employ audio signal segmentation in time intervals and also problem space decomposition. Initially the music signal is split in time segments; features are extracted from these music signal segments and the one against all (OAA) and round robin (RR) strategies, which implement a space decomposition by using several binary classifiers, are applied. Finally, the outputs of the set of classifiers are combined to produce the final result. We test our proposition in a music database of 1.200 music samples from four different music genres. Experimental results show that time segment decomposition is more important than the space decomposition produced by the OAA and RR strategies, although they produce better results relative to the use of single classifiers and feature vectors.

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: 16 Apr 2014 08:41
Resource URI: https://kar.kent.ac.uk/id/eprint/24095 (The current URI for this page, for reference purposes)
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