Evolutionary Computation Applied to Sound Synthesis

  • James McDermott
  • Niall J. L. Griffith
  • Michael O’Neill
Part of the Natural Computing Series book series (NCS)


Sound synthesis is a natural domain in which to apply evolutionary computation (EC). The EC concepts of the genome, the phenotype, and the fitness function map naturally to the synthesis concepts of control parameters, output sound, and comparison with a desired sound. More importantly, sound synthesis can be a very unintuitive technique, since changes in input parameters can give rise, via non-linearities and interactions among parameters, to unexpected changes in output sounds. The novice synthesizer user and the simple hill-climbing search algorithm will both fail to produce a desired sound in this context, whereas an EC technique is well-suited to the task.

In this chapter we introduce and provide motivation for the application of EC to sound synthesis, surveying previous work in this area. We focus on the problem of automatically matching a target sound using a given synthesizer. The ability to mimic a given sound can be used in several ways to augment interactive sound synthesis applications. We report on several sets of experiments run to determine the best EC algorithms, parameters, and fitness functions for this problem.


Random Search Parameter Distance Target Sound Evolutionary Computation Technique Sound Synthesis 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley. Reading, MAzbMATHGoogle Scholar
  2. 2.
    Grey, J.M. (1976). Multidimensional perceptual scaling of musical timbres. J. Acoust. Soc. Am., 61(5): 1270–1277CrossRefGoogle Scholar
  3. 3.
    McAdams, S., Cunibile, J.C. (1992). Perception of timbral analogies. Philosophical Transactions of the Royal Society, 336(Series B): 383–389Google Scholar
  4. 4.
    Horner, A., Beauchamp, J., Haken, L. (1993). Machine tongues XVI: Genetic algorithms and their application to FM matching synthesis. Computer Music Journal, 17(4): 17–29CrossRefGoogle Scholar
  5. 5.
    Riionheimo, J., Välimäki, V. (2003). Parameter estimation of a plucked string synthesis model using a genetic algorithm with perceptual fitness calculation. EURASIP Journal on Applied Signal Processing, 8: 791–805CrossRefGoogle Scholar
  6. 6.
    Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT PressGoogle Scholar
  7. 7.
    Wehn, K. (1998). Using ideas from natural selection to evolve synthesized sounds. In: Digital Audio Effects (DAFX) Google Scholar
  8. 8.
    Garcia, R.A. (2001). Growing sound synthesizers using evolutionary methods. In Bilotta, E., Miranda, E.R., Pantano, P., Todd, P., eds.: Proceedings ALMMA 2001: Artificial Life Models for Musical Applications Workshop (ECAL 2001) Google Scholar
  9. 9.
    Mitchell, T.J., Pipe, A.G. (2005). Convergence synthesis of dynamic frequency modulation tones using an evolution strategy. In Rothlauf, F., et al., eds.: EvoWorkshops 2005. Berlin Heidelberg. Springer, 533–538Google Scholar
  10. 10.
    Bolton, S. (2005). XSynth-DSSI. Last viewed 2 March 2006.Google Scholar
  11. 11.
    Jensen, K. (1999). Timbre Models of Musical Sounds. PhD thesis. Dept. of Computer Science, University of CopenhagenGoogle Scholar
  12. 12.
    Eronen, A., Klapuri, A. (2000). Musical instrument recognition using cepstral coefficients and temporal features. In: Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 753–756Google Scholar
  13. 13.
    Lu, L., Zhang, H.J., Jiang, H. (2002). Content analysis for audio classification and segmentation. IEEE Transactions on Speech and Audio Processing, 10(7): 504–516CrossRefGoogle Scholar
  14. 14.
    McDermott, J., Griffith, N.J., O’Neill, M. (2006). Timbral, perceptual, and statistical attributes for synthesized sound. In: Proceedings of the International Computer Music Conference 2006. International Computer Music AssociationGoogle Scholar
  15. 15.
    Luthman, L. (2005). Sineshaper. Last viewed 1 September 2006.Google Scholar
  16. 16.
    McDermott, J., Griffith, N.J., O’Neill, M. (2005). Toward user-directed evolution of sound synthesis parameters. In Rothlauf, F., et al., eds.: EvoWorkshops 2005. Berlin. SpringerGoogle Scholar
  17. 17.
    Jones, T., Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the 6th International Conference on Genetic Algorithms. San Francisco, CA, USA. Morgan Kaufmann Publishers Inc., 184–192Google Scholar
  18. 18.
    Gustafson, S.M., Hsu, W.H. (2001). Layered learning in genetic programming for a cooperative robot soccer problem. In Miller, J.F., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A., Langdon, W.B., eds.: Proceedings of EuroGP 2001. Springer, 291–301Google Scholar
  19. 19.
    Johnson, C.G. (2003). Exploring sound-space with interactive genetic algorithms. Leonardo, 36(1): 51–54CrossRefGoogle Scholar
  20. 20.
    Mandelis, J. (2001). Genophone: An evolutionary approach to sound synthesis and performance. In Bilotta, E., Miranda, E.R., Pantano, P., Todd, P., eds.: Proceedings ALMMA 2001: Artificial Life Models for Musical Applications Workshop Google Scholar
  21. 21.
    Dahlstedt, P. (2001). Creating and exploring huge parameter spaces: Interactive evolution as a tool for sound generation. In: Proceedings of the International Computer Music Conference 2001 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • James McDermott
    • 1
  • Niall J. L. Griffith
    • 2
  • Michael O’Neill
    • 3
  1. 1.University of LimerickLimerickIreland
  2. 2.University of LimerickLimerickIreland
  3. 3.University College DublinDublinIreland

Personalised recommendations