Abstract
In this paper we propose a novel time–space ensemble–based 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.
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Silla, C.N., Kaestner, C.A.A., Koerich, A.L. (2006). Time-Space Ensemble Strategies for Automatic Music Genre Classification. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_38
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DOI: https://doi.org/10.1007/11874850_38
Publisher Name: Springer, Berlin, Heidelberg
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