Stochastic Synthesizer Patch Exploration in Edisyn

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)


Edisyn is a music synthesizer program (or “patch”) editor library which enables musicians to easily edit and manipulate a variety of difficult-to-program synthesizers. Edisyn sports a first-in-class set of tools designed to help explore the parameterized space of synthesizer patches without needing to directly edit the parameters. This paper discusses the most sophisticated of these tools, Edisyn’s Hill-Climber and Constrictor methods, which are based on interactive evolutionary computation techniques. The paper discusses the special difficulties encountered in programming synthesizers, the motivation behind these techniques, and their design. It then evaluates them in an experiment with novice synthesizer users, and concludes with additional observations regarding utility and efficacy.


Synthesizer patch design Interactive evolutionary computation 



My thanks to Vankhanh Dinh, Bryan Hoyle, Palle Dahlstedt, and James McDermott for their considerable assistance in the development of this paper.


  1. 1.
    Horner, A., Beauchamp, J., Haken, L.: Musical tongues XVI: genetic algorithms and their application to FM matching synthesis. Comput. Music J. 17(4), 17–29 (1993)CrossRefGoogle Scholar
  2. 2.
    Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  3. 3.
    Biles, J.A.: GenJam: a genetic algorithm for generating jazz solos. In: ICMC, pp. 131–137 (1994)Google Scholar
  4. 4.
    McDermott, J., O’Neill, M., Griffith, N.J.L.: Interactive EC control of synthesized timbre. Evol. Comput. 18(2), 277–303 (2010)CrossRefGoogle Scholar
  5. 5.
    Seago, A.: A new interaction strategy for musical timbre design. In: Holland, S., Wilkie, K., Mulholland, P., Seago, A. (eds.) Music and Human-Computer Interaction, pp. 153–169. Springer, London (2013). Scholar
  6. 6.
    Suzuki, R., Yamaguchi, S., Cody, M.L., Taylor, C.E., Arita, T.: iSoundScape: adaptive walk on a fitness soundscape. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6625, pp. 404–413. Springer, Heidelberg (2011). Scholar
  7. 7.
    Dahlstedt, P.: A MutaSynth in parameter space: interactive composition through evolution. Organized Sound 6(2), 121–124 (2001)Google Scholar
  8. 8.
    Dahlstedt, P.: Evolution in creative sound design. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 79–99. Springer, London (2007). Scholar
  9. 9.
    Dahlstedt, P.: Thoughts of creative evolution: a meta-generative approach to composition. Contemp. Music Rev. 28(1), 43–55 (2009)CrossRefGoogle Scholar
  10. 10.
    Collins, N.: Experiments with a new customisable interactive evolution framework. Organised Sound 7(3), 267–273 (2002)CrossRefGoogle Scholar
  11. 11.
    Mandelis, J.: Genophone: evolving sounds and integral performance parameter mappings. In: Cagnoni, S., et al. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 535–546. Springer, Heidelberg (2003). Scholar
  12. 12.
    Yee-King, M.J.: The use of interactive genetic algorithms in sound design: a comparison study. Comput. Entertainment 14 (2016)Google Scholar
  13. 13.
    Ianigro, S., Bown, O.: Plecto: a low-level interactive genetic algorithm for the evolution of audio. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds.) EvoMUSART 2016. LNCS, vol. 9596, pp. 63–78. Springer, Cham (2016). Scholar
  14. 14.
    Jónsson, B., Hoover, A.K., Risi, S.: Interactively evolving compositional sound synthesis networks. In: GECCO, pp. 321–328 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.George Mason UniversityWashington, DCUSA

Personalised recommendations