Exploring the Design Space of Symbolic Music Genre Classification Using Data Mining Techniques

This paper describes a method based on data mining techniques to classify MIDI music files into music genres. Our method relies on extracting high level symbolic features from MIDI files. We explore the effect of combining several data mining preprocessing stages to reduce data processing complexity and classification execution time. Additionally, we employ a variety of probabilistic classifiers and ensembles. We compare the results produced by our best classifier with those obtained by more complex state of the art classifiers. Our experimental results indicate that our system constructed with the best performing combination of data mining preprocessing components together with a Naive Bayes-based classifier is capable of outperforming other more complex ensembles of classifiers.