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1. Ponce de León, P. J.; Iñesta, J. M
   "Musical Style Classification from Symbolic Data: A Two Styles Case Study"
   Selected Papers from the Proceedings of the Computer Music Modeling and Retrieval 2003, Lecture Notes in Computer Science, vol. 2771, pp. 167-177 (2004)
   : bibtex : pdf
   Abstract:

   In this paper the clasiffication of monophonic melodies from two different musical styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a kNN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in terms of separability in two music classes, obtaining several reduced descriptor sets. Finally, the classification results for each type of classifier for the different descriptor models are compared. This scheme has a number of applications like indexing and selecting musical databases or the evaluation of style-specific automatic composition systems.

@article {
 author = "Ponce de León, P. J.; Iñesta, J. M",
 title  = "Musical Style Classification from Symbolic Data: A Two Styles Case Study",
 issn = "0302-9743",
 journal = "Selected Papers from the Proceedings of the Computer Music Modeling and Retrieval 2003, Lecture Notes in Computer Science",
 pages = "167-177",
 volume = "2771",
 year = "2004"
}