Play it Again: Evolved Audio Effects and Synthesizer Programming

  • Benjamin D. SmithEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10198)


Automatic programming of sound synthesizers and audio devices to match a given, desired sound is examined and a Genetic Algorithm (GA) that functions independent of specific synthesis techniques is proposed. Most work in this area has focused on one synthesis model or synthesizer, designing the GA and tuning the operator parameters to obtain optimal results. The scope of such inquiries has been limited by available computing power, however current software (Ableton Live, herein) and commercially available hardware is shown to quickly find accurate solutions, promising a practical application for music creators. Both software synthesizers and audio effects processors are examined, showing a wide range of performance times (from seconds to hours) and solution accuracy, based on particularities of the target devices. Random oscillators, phase synchronizing, and filters over empty frequency ranges are identified as primary challenges for GA based optimization.


Sound synthesis Machine learning Adaptive genetic algorithms Audio effects 


  1. 1.
    Garcia, R.: Growing sound synthesizers using evolutionary methods. In: Proceedings ALMMA 2001: Artificial Life Models for Musical Applications Workshop (ECAL 2001) (2001)Google Scholar
  2. 2.
    Horner, A.: Double-modulator FM matching of instrument tones. Comput. Music J. 20(2), 57–71 (1996)CrossRefGoogle Scholar
  3. 3.
    Horner, A.: Nested modulator and feedback FM matching of instrument tones. IEEE Trans. Speech Audio Process. 6(4), 398–409 (1998)CrossRefGoogle Scholar
  4. 4.
    Horner, A., Beauchamp, J., Haken, L.: Machine tongues XVI: Genetic algorithms and their application to FM matching synthesis. Comput. Music J. 17(4), 17–29 (1993)CrossRefGoogle Scholar
  5. 5.
    Johnson, A., Phillips, I.: Sound Resynthesis with a Genetic Algorithm. Imperial College London (2011)Google Scholar
  6. 6.
    Karafotias, G., Hoogendoorn, M., Eiben, A.E.: Parameter control in evolutionary algorithms: Trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)CrossRefGoogle Scholar
  7. 7.
    Lai, Y., Liu, D.T., Jeng, S.K., Liu, Y.C.: Automated optimization of parameters for FM sound synthesis with genetic algorithms. In: Proceedings of the International Workshop on Computer Music and Audio Technology. Citeseer (2006)Google Scholar
  8. 8.
    Macret, M., Pasquier, P.: Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 309–316. ACM (2014)Google Scholar
  9. 9.
    Macret, M.M.J.: Automatic Tuning of the Op-1 Synthesizer Using a Multi-Objective Genetic Algorithm. Doctoral dissertation, Simon Fraiser University, Vancouver, CN (2013)Google Scholar
  10. 10.
    Riionheimo, J., Välimäki, V.: Parameter estimation of a plucked string synthesis model using a genetic algorithm with perceptual fitness calculation. EURASIP J. Adv. Signal Process. 8(1–15) (2003)Google Scholar
  11. 11.
    Rylander, S.G.: Optimal population size and the genetic algorithm. Population 100(400) (2002)Google Scholar
  12. 12.
    Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)CrossRefGoogle Scholar
  13. 13.
    Tan, B., Lim, S.: Automated parameter optimization of double frequency modulation synthesis using the genetic annealing algorithm. J. Audio Eng. Soc. 44(1/2), 3–15 (1996)Google Scholar
  14. 14.
    Tatar, K., Macret, M., Pasquier, P.: Automatic synthesizer preset generation with presetgen. J. New Music Res. 45(2), 124–144 (2016)CrossRefGoogle Scholar
  15. 15.
    Weise, T., Wu, Y., Chiong, R., Tang, K., Lässig, J.: Global versus local search: The impact of population sizes on evolutionary algorithm performance. J. Global Optim. 66(3), 1–24 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Yee-King, M., Roth, M.: Synthbot: An unsupervised software synthesizer programmer. In: Proceedings of the International Computer Music Conference, Ireland (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Indiana University-Purdue University-IndianapolisIndianapolisUSA

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