Evolving Music Generation with SOM-Fitness Genetic Programming

  • Somnuk Phon-Amnuaisuk
  • Edwin Hui Hean Law
  • Ho Chin Kuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4448)

Abstract

Most real life applications have huge search spaces. Evolutionary Computation provides an advantage in the form of parallel explorations of many parts of the search space. In this report, Genetic Programming is the technique we used to search for good melodic fragments. It is generally accepted that knowledge is a crucial factor to guide search. Here, we show that SOM can be used to facilitate the encoding of domain knowledge into the system. The SOM was trained with music of desired quality and was used as fitness functions. In this work, we are not interested in music with complex rules but with simple music employed in computer games. We argue that this technique provides a flexible and adaptive means to capture the domain knowledge in the system.

Keywords

Genetic Programming Self-Organising Features Map Automatic Music generation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Biles, J.A.: GenJam: a genetic algorithm for generating jazz solos. In: Proceedings of the International Computer Music Conference, pp. 131-137, Aarhus, Denmark (1994)Google Scholar
  2. 2.
    Burton, A.R., Vladimirava, T.: Generation of musical sequences with genetic techniques. Computer Music Journal 23(4), 59–73 (1999)CrossRefGoogle Scholar
  3. 3.
    Courtot, F.: Logical Representation and Induction for Computer Assisted Composition. In: Balaban, M., Ebcioglu, K., Laske, O. (eds.) Understanding Music with AI: Perspectives on music cognition 7, pages 157-181. The AAAI Press/The MIT Press (1992)Google Scholar
  4. 4.
    Gartland-Jones, A., Copley, P.: The suitability of genetic algorithms for musical composition. Contemporary Music Review 22(3), 43–55 (2003)CrossRefGoogle Scholar
  5. 5.
    Ebcioglu, K.: An expert system for harmonizing four-part chorales. In: Balaban, M., Ebcioglu, K., Laske, O. (eds.) Understanding Music with AI: Perspectives on music cognition, Chapter 12, pp. 294-333. The AAAI Press/The MIT PressGoogle Scholar
  6. 6.
    Horner, A., Goldberg, D.E.: Genetic algorithms and computer-assisted music composition. In: Belew, R., Booker, L. (eds.) The Fourth International Conference on Genetic Algorithms, Morgan Kauffman, San Francisco, CA (1991)Google Scholar
  7. 7.
    Kohonen, T.: Self-organising Maps, 2nd edn. Springer-Verlag, Berlin Heidelberg New York (1997)CrossRefMATHGoogle Scholar
  8. 8.
    Koza, J.R.: Genetic Programming: On Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, MA (1992)MATHGoogle Scholar
  9. 9.
    Miranda, E.R.: On the music of emergent behaviour: what can evolutionary computation bring to musician?. Leodarno 36(1), 55–88 (2003)Google Scholar
  10. 10.
    Phon-Amnuaisuk, S.: Control language for harmonisation process. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds.) ICMAI 2002. LNAI (LNCS), vol. 2445, Springer, Berlin Heidelberg New York (2002)CrossRefGoogle Scholar
  11. 11.
    de León, P.J.P., Inesta, J.M.: Musical style classification from symbolic data: A two-styles case study. In: Wiil, U.K. (ed.) CMMR 2003. LNCS, vol. 2771, pp. 166–177. Springer, Berlin Heidelberg New York (2004)Google Scholar
  12. 12.
    Skovenborg, E., Arnspang, J.: Extraction of structural patterns in popular melodies. In: Wiil, U.K. (ed.) CMMR 2003. LNCS, vol. 2771, pp. 98–113. Springer, Berlin Heidelberg New York (2004)CrossRefGoogle Scholar
  13. 13.
    Temperley, D.: The Cognition of Basic Musical Structure. The MIT Press, Cambridge, MA (2001)Google Scholar
  14. 14.
    Todd, P.M., Werner, G.M.: Frankensteinian methods for evolutionary music composition. In: N. Griffith, P. M. Todd, (eds.) Musical Networks: Parallel Distributed Perception and Performance, pp. 313-340, The MIT PressGoogle Scholar
  15. 15.
    Toiviainen, P., Eerola, T.A: method for comparative analysis of folk music based on musical feature extraction and neural networks. In: VII International Symposium on Systematic and Comparative Musicology and III International Conference on Cognitive Musicology, University of Jyvskyl, Finland (August 16-19, 2001)Google Scholar
  16. 16.
    West, R., Howell, P., Cross, I.: Musical structure and knowledge representation. In: West, R., Howell, P., Cross, I. (eds.) Representing Musical Structure, vol. 1, pp. 1–30. Academic Press, San Diego (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Somnuk Phon-Amnuaisuk
    • 1
  • Edwin Hui Hean Law
    • 1
  • Ho Chin Kuan
    • 1
  1. 1.Music Informatics Research Group, Faculty of Information Technology, Multimedia University, Jln Multimedia, 63100 Cyberjaya, Selangor Darul EhsanMalaysia

Personalised recommendations