Talks and Poster Presentations (with Proceedings-Entry):
T. Lidy, R. Mayer, A. Rauber, P.J. Ponce de Leon, A. Pertusa, J. Iñesta:
"A cartesian ensemble of feature subspace classifiers for music categorization";
Talk: International Conference on Music Information Retrieval (ISMIR),
Utrecht, The Netherlands;
- 2010-08-14; in: "Proceedings of the International Society for Music Information Retrieval Conference (ISMIR 2010)",
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.
music classification, ensemble, music retrieval, genre classification, cartesian
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.