Advertisement

A Differential Evolution Algorithm Assisted by ANFIS for Music Fingering

  • Roberto De Prisco
  • Gianluca Zaccagnino
  • Rocco Zaccagnino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

Music fingering is a cognitive process whose goal is to map each note of a music score to a fingering on some instrument. A fingering specifies the fingers of the hands that the player should use to play the notes. This problem arises for many instruments and it can be quite different from instrument to instrument; guitar fingering, for example, is different from piano fingering. Previous work focuses on specific instruments, in particular the guitar, and evolutionary algorithms have been used.

In this paper, we propose a differential evolution (DE) algorithm designed for general music fingering (any kind of music instruments). The algorithm uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that learns the fingering from music already fingered.

The algorithm follows the basic DE strategy but exploits also some customizations specific to the fingering problem. We have implemented the DE algorithm in Java and we have used the ANFIS network in Matlab. The two systems communicate by using the MatlabControl library. Several tests have been performed to evaluate its efficacy.

Keywords

Fuzzy Inference System ANFIS Model Trial Vector Music Score Memetic Comp 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    De Prisco, R., Zaccagnino, G., Zaccagnino, R.: EvoBassComposer: a multi-objective genetic algorithm for 4-voice compositions. In: Proceedings of the 12th ACM Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 817–818 (2010)Google Scholar
  2. 2.
    De Prisco, R., Zaccagnino, G., Zaccagnino, R.: A multi-objective differential evolution algorithm for 4-voice compositions. In: Proceedings of IEEE Symposium on Differential Evolution, SDE 2011, pp. 817–818 (2011)Google Scholar
  3. 3.
    Emura, N., Miura, M., Hama, N., Yanagida, M.: A system giving the optimal chordform sequence for playing a guitar. Acoustical Science and Technology (January 2006)Google Scholar
  4. 4.
    Kandel, A.: Fuzzy expert systems. CRC Press, Boca Raton (1992)Google Scholar
  5. 5.
    Jang, J.R.: ANFIS: An Adaptive-Nework-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3) (May/June 1993)Google Scholar
  6. 6.
    Miura, M., Yanagida, M.: Finger-position determination and tablature generation for novice guitar players. In: Proceedings of the 7th International Conference on Music Perception and Cognition (2002)Google Scholar
  7. 7.
    Neri, F., Tirronen, V.: Scale factor local search in differential evolution. Memetic Comp. 1, 153–171 (2009)CrossRefGoogle Scholar
  8. 8.
    Neri, F., Tirronen, V.: Recent Advances in Differential Evolution: A Review and Experimental Analysis. Artificial Intelligence Review 33(1), 61–106Google Scholar
  9. 9.
    Radisavljevic, A., Driessen, P.: Path difference learning for guitar fingering problem. In: Proceedings of the International Computer Music Conference (2004)Google Scholar
  10. 10.
    Sayegh, S.: Fingering for string instruments with the optimum path paradigm. Computer Music Journal 6(13), 76–84 (1989)CrossRefGoogle Scholar
  11. 11.
    Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, BerkeleyGoogle Scholar
  12. 12.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. Proceedings of IEEE Transactions on Systems, Man, and Cybernetics 15, 116–132 (1985)zbMATHGoogle Scholar
  13. 13.
    Tuohy, D., Potter, W.: A genetic algorithm for the automatic generation of playable guitar tablature. In: Proceedings of the International Computer Music Conference (2004)Google Scholar
  14. 14.
    Tuohy, D., Potter, W.: GA-based music arranging for guitar. In: Procedings of IEEE Congress on Evolutionary Computation, CEC 2006 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roberto De Prisco
    • 1
  • Gianluca Zaccagnino
    • 1
  • Rocco Zaccagnino
    • 1
  1. 1.Musimathics Laboratory Dipartimento di InformaticaUniversità di SalernoFiscianoItaly

Personalised recommendations