A cartesian ensemble of feature subspace classifiers for music categorization

T. Lidy, R. Mayer, A. Rauber, P.J. Ponce de Leon, A. Pertusa, J. IƱesta:
"A cartesian ensemble of feature subspace classifiers for music categorization";
Vortrag: International Conference on Music Information Retrieval (ISMIR), Utrecht, The Netherlands; 09.08.2010 - 14.08.2010; in:"Proceedings of the International Society for Music Information Retrieval Conference (ISMIR 2010)", (2010), S. 279 - 284.

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Abstract:


This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.