A Shallow Description Framework for Musical Style Recognition

  • Pedro J. Ponce de León
  • Carlos Pérez-Sancho
  • José M. Iñesta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval (MIR). One challenging task within this area is the automatic recognition of musical style, that has a number of applications like indexing and selecting musical databases. In this paper, the classification of monophonic melodies of two different musical styles (jazz and classical) represented symbolically as MIDI files is studied, using different classification methods: Bayesian classifier and nearest neighbour classifier. From the music sequences, a number of melodic, harmonic, and rhythmic statistical descriptors are computed and used for style recognition. We present a performance analysis of such algorithms against different description models and parameters.

Keywords

music information retrieval Bayesian classifier nearest neighbours 

References

  1. 1.
    Pampalk, E., Dixon, S., Widmer, G.: Exploring music collections by browsing different views. In: Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR 2003), Baltimore, USA, pp. 201–208 (2003)Google Scholar
  2. 2.
    Whitman, B., Flake, G., Lawrence, S.: Artist detection in music with minnowmatch. In: Proceedings of the 2001 IEEE Workshop on Neural Networks for Signal Processing, Falmouth, Massachusetts, September 10-12, pp. 559–568 (2001)Google Scholar
  3. 3.
    Soltau, H., Schultz, T., Westphal, M., Waibel, A.: Recognition of music types. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1998), Seattle, Washington (May 1998)Google Scholar
  4. 4.
    Thom, B.: Unsupervised learning and interactive jazz/blues improvisation. In: Proceedings of the AAAI 2000, pp. 652–657 (2000)Google Scholar
  5. 5.
    Toiviainen, P., Eerola, T.: Method for comparative analysis of folk music based on musical feature extraction and neural networks. In: III International Conference on Cognitive Musicology, Jyvskyl, Finland, pp. 41–45 (2001)Google Scholar
  6. 6.
    Cruz-Alcázar, P.P., Vidal, E., Pérez-Cortes, J.C.: Musical style identification using grammatical inference: The encoding problem. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 375–382. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Chai, W., Vercoe, B.: Folk music classification using hidden markov models. In: Proc. of the Int. Conf. on Artificial Intelligence, Las Vegas, USA (2001)Google Scholar
  8. 8.
    Buzzanca, G.: A supervised learning approach to musical style recognition. In: Music and Artificial Intelligence. Additional Proceedings of the Second International Conference, ICMAI 2002, Edinburgh, Scotland (2002)Google Scholar
  9. 9.
    Ponce de León, P.J., Iñesta, J.M.: Feature-driven recognition of music styles. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 773–781. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Pickens, J.: A survey of feature selection techniques for music information retrieval. Technical report, Center for Intelligent Information Retrieval, Departament of Computer Science, University of Massachussetts (2001)Google Scholar
  11. 11.
    Blackburn, S.G.: Content Based Retrieval and Navigation of Music Using Melodic Pitch Contours. PhD thesis, Department of Electronics and Computer Science, University of Southampton, UK (2000)Google Scholar
  12. 12.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, vol. 2. Wiley-Interscience, Hoboken (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pedro J. Ponce de León
    • 1
  • Carlos Pérez-Sancho
    • 1
  • José M. Iñesta
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

Personalised recommendations