A Machine Learning Approach to Automatic Music Genre Classification

Carlos N. Silla Jr.Alessandro L. KoerichCelso A. A. Kaestner

This paper presents a non-conventional approach forthe automatic music genre classification problem. Theproposed approach uses multiple feature vectors anda pattern recognition ensemble approach, according tospace and time decomposition schemes. Despite beingmusic genre classification a multi-class problem, we accomplishthe task using a set of binary classifiers, whoseresults are merged in order to produce the final musicgenre label (space decomposition). Music segments arealso decomposed according to time segments obtainedfrom the beginning, middle and end parts of the originalmusic signal (time-decomposition). The final classificationis obtained from the set of individual results, accordingto a combination procedure. Classical machine learningalgorithms such as Naïve-Bayes, Decision Trees, kNearest-Neighbors, Support Vector Machines and Multi-Layer Perceptron Neural Nets are employed. Experimentswere carried out on a novel dataset called Latin MusicDatabase, which contains 3,160 music pieces categorizedin 10 musical genres. Experimental results show thatthe proposed ensemble approach produces better resultsthan the ones obtained from global and individual segmentclassifiers in most cases. Some experiments relatedto feature selection were also conducted, using the geneticalgorithm paradigm. They show that the most importantfeatures for the classification task vary according to theirorigin in the music signal.

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