Towards Music Fitness Evaluation with the Hierarchical SOM

  • Edwin Hui Hean Law
  • Somnuk Phon-Amnuaisuk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)


In any evolutionary search system, the fitness raters are most crucial in determining successful evolution. In this paper, we propose a Hierarchical Self Organizing Map based sequence predictor as a fitness evaluator for a music evolution system. The hierarchical organization of information in the HSOM allows prediction to be performed with multiple levels of contextual information. Here, we detail the design and implementation of such a HSOM system. From the experimental setup, we show that the HSOM’s prediction performance exceeds that of a Markov prediction system when using randomly generated and musical phrases.


Fitness Evaluation for Music Generation Self Organizing Map Hierarchical SOM 


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  1. 1.
    Cambouropoulos, E.: Towards a General Computational Theory of Musical Structure. PhD Thesis, University of Edinburgh (1998)Google Scholar
  2. 2.
    Cope, D.: Virtual Music: Computer Synthesis on Musical Style. MIT Press, Cambridge (2004)Google Scholar
  3. 3.
    Conkin, D.: Representation and Discovery of Vertical Patterns in Music. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds.) ICMAI 2002. LNCS (LNAI), vol. 2445, pp. 33–42. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Conklin, D., Witten, I.: Multiple viewpoint systems for music prediction. Journal of New Music Research 24, 51–73 (1995)CrossRefGoogle Scholar
  5. 5.
    Forte, A., Gilbert, S.E.: Introduction to Schenkerian Analysis. W. W. Norton, New York (1982)Google Scholar
  6. 6.
    George, D., Hawkins, J.: A bayesian model of invariant pattern recognition in the visual cortex. In: Proceedings of the International Conference on Neural Networks, Montreal, Canada (2006)Google Scholar
  7. 7.
    Hawkins, J., Blakeslee, S.: On intelligence. Henry Holt, New York (2004)Google Scholar
  8. 8.
    Kohonen, T.: Self-organising Maps, 2nd edn. Springer, Heidelberg (1997)Google Scholar
  9. 9.
    Lampinen, J., Oja, E.: Clustering Properties of Hierarchical Self-Organizing Maps. Journal of Mathematical Imaging and Vision 2, 261–272 (1992)zbMATHCrossRefGoogle Scholar
  10. 10.
    Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1983)Google Scholar
  11. 11.
    Mozer, M.C.: Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Contraints and Multi-scale Processing. In: Griffith, N., Todd, P.M. (eds.) Musical Networks, pp. 227–260. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Narmour, E.: The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model. University of Chicago Press, Chicago (1990)Google Scholar
  13. 13.
    Phon-Amnuaisuk, S., Law, E.H.H., Ho, C.K.: Evolving Music Generation with SOM-Fitness Genetic Programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 557–566. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Edwin Hui Hean Law
    • 1
  • Somnuk Phon-Amnuaisuk
    • 1
  1. 1.Music Informatics Research Group, Faculty of Information TechnologyMultimedia University, Jln MultimediaCyberjayaMalaysia

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