Plecto: A Low-Level Interactive Genetic Algorithm for the Evolution of Audio

  • Steffan IanigroEmail author
  • Oliver Bown
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9596)


The creative potential of Genetic Algorithms (GAs) has been explored by many musicians who attempt to harness the unbound possibilities for creative search evident in nature. Within this paper, we investigate the possibility of using Continuous Time Recurrent Neural Networks (CTRNNs) as an evolvable low-level audio synthesis structure, affording users access to a vast creative search space of audio possibilities. Specifically, we explore some initial GA designs through the development of Plecto (see, a creative tool that evolves CTRNNs for the discovery of audio. We have found that the evolution of CTRNNs offers some interesting prospects for audio exploration and present some design considerations for the implementation of such a system.


Neural network Genetic Algorithm Evolution Interaction design 


  1. 1.
    Dahlstedt, P., Nordahl, M.G.: Living melodies: coevolution of sonic communication. Leonardo 34(3), 243–248 (2001)CrossRefGoogle Scholar
  2. 2.
    McCormack, J.: Facing the future: evolutionary possibilities for human-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, pp. 417–451. Springer, New York (2008)CrossRefGoogle Scholar
  3. 3.
    McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Bown, O.: Empirically grounding the evaluation of creative systems: incorporating interaction design. In: Proceedings of the Fifth International Conference on Computational Creativity (2014)Google Scholar
  5. 5.
    Husbands, P., Copley, P., Eldridge, A., Mandelis, J.: An introduction to evolutionary computing for musicians. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 1–27. Springer, New York (2007)CrossRefGoogle Scholar
  6. 6.
    Tzimeas, D., Mangina, E.: Dynamic techniques for genetic algorithm-based music systems. Comput. Music J. 33(3), 45–60 (2009)CrossRefGoogle Scholar
  7. 7.
    Bown, O.: Ecosystem models for real-time generative music: A methodology and framework. In: International Computer Music Conference (Gary Scavone 16 to 21 August 2009), The International Computer Music Association, pp. 537–540, August 2009Google Scholar
  8. 8.
    Tokui, N., Iba, H.: Music composition with interactive evolutionary computation. In: Proceedings of the 3rd International Conference on Generative Art, vol. 17, pp. 215–226 (2000)Google Scholar
  9. 9.
    Woolf, S., Yee-King, M.: Virtual and physical interfaces for collaborative evolution of sound. Contemp. Music Rev. 22(3), 31–41 (2003)CrossRefGoogle Scholar
  10. 10.
    Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: a case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)CrossRefGoogle Scholar
  11. 11.
    Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: ALIFE, pp. 329–336 (2008)Google Scholar
  12. 12.
    Yee-King, M., Roth, M.: Synthbot: an unsupervised software synthesizer programmer. In: Proceedings of the International Computer Music Conference, Ireland (2008)Google Scholar
  13. 13.
    Yee-King, M.J.: An automated music improviser using a genetic algorithm driven synthesis engine. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 567–576. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    MacCallum, R.M., Mauch, M., Burt, A., Leroi, A.M.: Evolution of music by public choice. Proc. Nat. Acad. Sci. 109(30), 12081–12086 (2012)CrossRefGoogle Scholar
  15. 15.
    Magnus, C., Cal IT CRCA: Evolving electroacoustic music: the application of genetic algorithms to time-domain waveforms. In: Proceedings of the 2004 International Computer Music Conference, pp. 173–176. Citeseer (2004)Google Scholar
  16. 16.
    Biles, J., Anderson, P., Loggi, L.: Neural network fitness functions for a musical IGA (1996)Google Scholar
  17. 17.
    Mozer, M.C.: Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Sci. 6(2–3), 247–280 (1994)CrossRefGoogle Scholar
  18. 18.
    Bown, O., Lexer, S.: Continuous-time recurrent neural networks for generative and interactive musical performance. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 652–663. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Ohya, K.: A sound synthesis by recurrent neural network. In: Proceedings of the 1995 International Computer Music Conference, pp. 420–423 (1995)Google Scholar
  20. 20.
    Eldridge, A.: Neural oscillator synthesis: Generating adaptive signals with a continuous-time neural modelGoogle Scholar
  21. 21.
    Beer, R.D.: On the dynamics of small continuous-time recurrent neural networks. Adapt. Behav. 3(4), 469–509 (1995)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Blanco, A., Delgado, M., Pegalajar, M.: A genetic algorithm to obtain the optimal recurrent neural network. Int. J. Approximate Reasoning 23(1), 67–83 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Machado, P., Martins, T., Amaro, H., Abreu, P.H.: An interface for fitness function design. In: Romero, J., McDermott, J., Correia, J. (eds.) EvoMUSART 2014. LNCS, vol. 8601, pp. 13–25. Springer, Heidelberg (2014)Google Scholar
  24. 24.
    Jordà, S.: Faust music on line: An approach to real-time collective composition on the internet. Leonardo Music J. 9, 5–12 (1999)CrossRefGoogle Scholar
  25. 25.
    McCormack, J.: Evolving sonic ecosystems. Kybernetes 32(1/2), 184–202 (2003)CrossRefGoogle Scholar
  26. 26.
    Routen, T.: Techniques for the visualisation of genetic algorithms. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994, IEEE World Congress on Computational Intelligence, pp. 846–851. IEEE (1994)Google Scholar
  27. 27.
    Mach, M.Z., Zetakova, M.: Visualising genetic algorithms: a way through the labyrinth of search space. In: Sincak, P., Vascak, J., Kvasnicak, V., Pospichal, J. (eds.) Intelligent Technologies-Theory and Applications, pp. 279–285. IOS Press, Amsterdam (2002)Google Scholar
  28. 28.
    Schedl, M., Höglinger, C., Knees, P.: Large-scale music exploration in hierarchically organized landscapes using prototypicality information. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 8. ACM (2011)Google Scholar
  29. 29.
    Schwarz, D.: The sound space as musical instrument: playing corpus-based concatenative synthesis. New Interfaces for Musical Expression (NIME), pp. 250–253 (2012)Google Scholar
  30. 30.
  31. 31.
    Nelson, G.L.: Sonomorphs: An application of genetic algorithms to the growth and development of musical organisms. In: Proceedings of the Fourth Biennial Art & Technology Symposium, vol. 155 (1993)Google Scholar
  32. 32.
    Darwin Tunes.
  33. 33.
  34. 34.
    Piamonte, D.P.T., Abeysekera, J.D., Ohlsson, K.: Understanding small graphical symbols: a cross-cultural study. Int. J. Ind. Ergon. 27(6), 399–404 (2001)CrossRefGoogle Scholar
  35. 35.
    Dahlstedt, P.: Creating and exploring huge parameter spaces: Interactive evolution as a tool for sound generation. In: Proceedings of the 2001 International Computer Music Conference, pp. 235–242 (2001)Google Scholar
  36. 36.
    Gohlke, K., Hlatky, M., Heise, S., Black, D., Loviscach, J.: Track displays in daw software: beyond waveform views. In: Audio Engineering Society Convention 128, Audio Engineering Society (2010)Google Scholar
  37. 37.
  38. 38.
  39. 39.
    Yu, G., Slotine, J.J.: Audio classification from time-frequency texture. arXiv preprint arxiv:0809.4501 (2008)
  40. 40.
  41. 41.
    Bown, O., McCormack, J.: Taming nature: tapping the creative potential of ecosystem models in the arts. Digital Creativity 21(4), 215–231 (2010)CrossRefGoogle Scholar
  42. 42.
    Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connection Sci. 6(2–3), 325–354 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Architecture, Design and PlanningUniversity of SydneyDarlingtonAustralia
  2. 2.Art and DesignUniversity of New South WalesPaddingtonAustralia

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