Musical Style Classification from Symbolic Data: A Two-Styles Case Study

  • Pedro J. Ponce de León
  • José M. Iñesta
Conference paper

DOI: 10.1007/978-3-540-39900-1_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2771)
Cite this paper as:
de León P.J.P., Iñesta J.M. (2004) Musical Style Classification from Symbolic Data: A Two-Styles Case Study. In: Wiil U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2003. Lecture Notes in Computer Science, vol 2771. Springer, Berlin, Heidelberg

Abstract

In this paper the classification of monophonic melodies from two different musical styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a k-NN 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.

Keywords

music information retrieval self-organising maps bayesian classifier nearest neighbours (k-NN) feature selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pedro J. Ponce de León
    • 1
  • José M. Iñesta
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de Alicante, Ap. 99AlicanteSpain

Personalised recommendations