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Ants Can Play Music

  • Christelle Guéret
  • Nicolas Monmarché
  • Mohamed Slimane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)

Abstract

In this paper, we describe how we can generate music by simulating moves of artificial ants on a graph where vertices represent notes and edges represent possible transitions between notes. As ants can deposit pheromones on edges, they collectively build a melody which is a sequence of Midi events. Different parameter settings are tested to produce different styles of generated music with several instruments. We also introduce a mechanism that takes into account music files to initialize the pheromone matrix.

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References

  1. 1.
    Aupetit, S., Bordeau, V., Monmarché, N., Slimane, M., Venturini, G.: Interactive Evolution of Ant Paintings. In: IEEE Congress on Evolutionary Computation, Canberra, December 8-12, vol. 2, pp. 1376–1383. IEEE Press, Los Alamitos (2003)Google Scholar
  2. 2.
    Bentley, P., Corne, D. (eds.): Creative Evolutionary Systems. Morgan Kaufmann, San Francisco (2001)Google Scholar
  3. 3.
    Biles, J.: GEMJAM: a genetic algorithm for generating jazz solos. In: Proceedings of the International Computer Music Conference, San Francisco. International Computer Music Association, pp. 131–137 (1994)Google Scholar
  4. 4.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  5. 5.
    Chen, C., Miikkulainen, R.: Creating melodies with evolving recurrent neural networks. In: Proceedings of the 2001 International Joint Conference on Neural Networks (IJCNN-2001), pp. 2241–2246 (2001)Google Scholar
  6. 6.
    Cope, D.: Pattern matching as an engine for the computer simulation of musical style. In: I. C. M. Association, editor, Proceedings of the International Computer Music Conference, San Francisco, pp. 288–291 (1990)Google Scholar
  7. 7.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  8. 8.
    Eck, D., Schmidhuber, J.: Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In: Bourlard, H. (ed.) Proc. of IEEE Workshop on Neural Networks for Signal Processing XII, pp. 747–756 (2002)Google Scholar
  9. 9.
    Franklin, J.: Multi-phase learning for jazz improvisation and interaction. In: Proceedings of the eighth Biennal Symposium on Art and Technology, New London, Connecticut, March 1-3 (2001)Google Scholar
  10. 10.
    Henz, M., Lauer, S., Zimmermann, D.: COMPOzE – intention-based music composition through constraint programming. In: Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence, Toulouse, France, November 16–19, pp. 118–121. IEEE Computer Society Press, Los Alamitos (1996)CrossRefGoogle Scholar
  11. 11.
    Johnson, T.: Self-Similar Melodies. Edition 75 (1996)Google Scholar
  12. 12.
    Lesbros, V.: From images to sounds, a dual representation. Computer Music Journal 20(3), 59–69 (1996)CrossRefGoogle Scholar
  13. 13.
    Lopez de Mantaras, R., Arcos, J.: AI and music, from composition to expressive performance. AI magazine 23(3), 43–57 (2002)Google Scholar
  14. 14.
    Minsky, M.: Music, Mind and Meaning. In: Schwanauer, S., Levitt, D. (eds.) Machine Models of Music, pp. 327–354. MIT Press, Cambridge (1993)Google Scholar
  15. 15.
    Monmarché, N., Slimane, M., Venturini, G.: On improving clustering in numerical databases with artificial ants. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 13–17. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Monmarché, N., Venturini, G., Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16(8), 937–946 (2000)CrossRefGoogle Scholar
  17. 17.
    Morales-Manzanares, R., Morales, E., Danenberg, R., Berger, J.: SICIB: An interactive music composition system using body movements. New Music Research 25(2), 25–36 (2001)Google Scholar
  18. 18.
    Moroni, A., Manzolli, J., Von Zuben, F., Gudwin, R.: Vox populi: An interactive evolutionary system for algorithmic music composition. Leonardo Music Journal 10, 49–54 (2000)CrossRefGoogle Scholar
  19. 19.
    Root-Bernstein, R.: Music, creativity and scientific thinking. Leonardo 34(1), 63–68 (2001)CrossRefGoogle Scholar
  20. 20.
    Todd, P., Miranda, E.: Putting some (artificial) life into models of musical creativity. In: Deliege, I., Wiggins, G. (eds.) Musical creativity: Current research in theory and practise, Psychology Press (2003)Google Scholar
  21. 21.
    Todd, P., Werner, G.: Frankensteinian Methods for Evolutionary Music Composition. In: Griffith, N., Todd, P. (eds.) Musical Networks: Parallel distributed perception and performance, MIT Press/Bradford Books (1998)Google Scholar
  22. 22.
    Wulfhorst, R.D., Flores, L.V., Nakayama, L., Flores, C.D., Alvares, L.O.C., Viccari, R.M.: An open architecture for a musical multi-agent system. In: Proceedings of Brazilian symposium on computer music (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Christelle Guéret
    • 1
  • Nicolas Monmarché
    • 1
  • Mohamed Slimane
    • 1
  1. 1.Département InformatiqueLaboratoire d’Informatique de l’Université de Tours, École Polytechnique de l’Université de ToursToursFrance

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