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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 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 339-348. ISBN 978-3-540-45462-5. E-ISBN 978-3-540-45464-9. (doi:10.1007/11874850_38) (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) (KAR id:24095)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/11874850_38

Abstract

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: Book section
DOI/Identification number: 10.1007/11874850_38
Uncontrolled keywords: Support Vector Machine, Ensemble Method, Round Robin, Space Decomposition, Music Genre
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:14 UTC
Last Modified: 16 Feb 2021 12:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/24095 (The current URI for this page, for reference purposes)
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