Advertisement

Dynamical Music with Musical Boolean Networks

  • George Gabriel
  • Susan Stepney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10783)

Abstract

An extended Boolean network model is investigated as a possible medium in which a human composer can write music. A Boolean network is a simple discrete-time dynamical system whose state is characterised by the states of its constituent Boolean-valued vertices. The evolution of the system is predetermined by an initial state and the properties of the activation functions associated with each vertex. By associating musical events with the states of the system, its trajectory from a particular start state can be interpreted as a piece of tonal music. The primary source of interest in composing music using a deterministic dynamical system is the dependence of the musical result on the initial conditions. This paper explores the possibility of producing musically interesting variations on a given melodic phrase by changing the initial conditions from which the generating dynamical system is started.

Keywords

Music Dynamical systems Boolean networks Computer-assisted composition 

Notes

Acknowledgements

This work was funded by a Laidlaw Undergraduate Research and Leadership Scholarship.

References

  1. 1.
    Ames, C.: The Markov process as a compositional model: a survey and tutorial. Leonardo 22(2), 175–187 (1989)CrossRefGoogle Scholar
  2. 2.
    Boulanger-Lewandowski, N., Bengio, Y., Vincent, P.: Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392 (2012)
  3. 3.
    Brin, M., Stuck, G.: Introduction to Dynamical Systems. Cambridge University Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Burraston, D., Edmonds, E.: Cellular automata in generative electronic music and sonic art: a historical and technical review. Digit. Creat. 16(3), 165–185 (2005)CrossRefGoogle Scholar
  5. 5.
    Burrows, D.: A dynamical systems perspective on music. J. Musicol. 15(4), 529–545 (1997)CrossRefGoogle Scholar
  6. 6.
    Dorin, A.: Boolean networks for the generation of rhythmic structure. In: Proceedings of the Australian Computer Music Conference, vol. 38, p. 45 (2000)Google Scholar
  7. 7.
    Fernández, J.D., Vico, F.: AI methods in algorithmic composition: a comprehensive survey. J. Artif. Intell. Res. 48, 513–582 (2013)MathSciNetGoogle Scholar
  8. 8.
    Fowler, M.: Domain Specific Languages. Addison-Wesley, Boston (2010)Google Scholar
  9. 9.
    Ghedini, F., Pachet, F., Roy, P.: Creating music and texts with flow machines. In: Corazza, G.E., Agnoli, S. (eds.) Multidisciplinary Contributions to the Science of Creative Thinking. CTFC, pp. 325–343. Springer, Singapore (2016).  https://doi.org/10.1007/978-981-287-618-8_18 CrossRefGoogle Scholar
  10. 10.
    Hiller, L.A.: Computer music. Sci. Am. 201(6), 109–121 (1959)CrossRefGoogle Scholar
  11. 11.
    Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Miranda, E.R. (ed.): Guide to Unconventional Computing for Music. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-49881-2 Google Scholar
  13. 13.
    Miranda, E.R., Biles, J.A. (eds.): Evolutionary Computer Music. Springer, London (2007).  https://doi.org/10.1007/978-1-84628-600-1 Google Scholar
  14. 14.
    Mortveit, H., Reidys, C.: An Introduction to Sequential Dynamical Systems. Springer, New York (2007).  https://doi.org/10.1007/978-0-387-49879-9 zbMATHGoogle Scholar
  15. 15.
    Stepney, S.: Nonclassical computation – a dynamical systems perspective. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, Chap. 59, vol. 4, pp. 1979–2025. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-540-92910-9_59 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

Personalised recommendations