New trends in musical genre classification using optimum-path forest
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Abstract
Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.
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Keywords
Bayesian classifier, Hybrid feature selections, Machine learning techniques, Musical genre classification, Optimum-path forests, Pattern recognition techniques, Social Networks, Speed up, Experiments, Feature extraction, Forestry, Information retrieval, Learning systems, Classification (of information), Classification, Experimentation, Information Retrieval
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English
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Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, p. 699-704.
