Genomic: Evolving Sound Treatments Using Genetic Algorithms

  • Thomas M. Stoll
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8601)

Abstract

There are many systems for the evolution of creative musical material, that create and/or manipulate musical score data or synthesis parameters with a variety of techniques. This paper aims to add the technique of corpus-based sound sampling and processing to the list of applications used in conjunction with genetic algorithms. Genomic, a simple system for evolving sound treatment parameters, is presented, along with two simple use cases. Finally, a more complex process is outlined where sound treatment parameters are evolved and stored in a database with associated metadata for further organization and compositional use.

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References

  1. 1.
    Miranda, E.R., Biles, A. (eds.): Evolutionary Computer Music. Springer (2007)Google Scholar
  2. 2.
    Dahlstedt, P.: Evolution in Creative Sound Design. In: Evolutionary Computer Music, pp. 79–99. Springer (2007)Google Scholar
  3. 3.
    Biles, J.: GenJam: A genetic algorithm for generating jazz solos. In: Proceedings of the International Computer Music Conference, pp. 131–137 (1994)Google Scholar
  4. 4.
    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
  5. 5.
    Miranda, E.R.: Evolving cellular automata music: From sound synthesis to composition. In: Proceedings of the 2001 Workshop on Artificial Life Models for Musical Applications (2001)Google Scholar
  6. 6.
    Caetano, M., Rodet, X.: Independent manipulation of high-level spectral envelope shape features for sound morphing by means of evolutionary computation. In: Proceedings of the 13th International Conference on Digital Audio Effects (DAFx), vol. 21 (2010)Google Scholar
  7. 7.
    Arfib, D., Keiler, F., Zölzer, U.: Source-filter processing. DAFX: Digital Audio Effects, 279–329 (2002)Google Scholar
  8. 8.
    Terasawa, H., Slaney, M., Berger, J.: Perceptual distance in timbre space. In: Proceedings of the International Conference on Auditory Display (ICAD 2005), pp. 61–68 (2005)Google Scholar
  9. 9.
    Johnson, C.: Exploring the sound-space of synthesis algorithms using interactive genetic algorithms. In: Proceedings of the AISB 1999 Symposium on Musical Creativity, pp. 20–27 (1999)Google Scholar
  10. 10.
    Casals, D.P.: Remembering the future: genetic co-evolution and MPEG7 matching in the creation of artificial music improvisors. Ph.D. Thesis, University of East Anglia (2008)Google Scholar
  11. 11.
    McCormack, J., Bown, O.: Life?s What You Make: Niche Construction and Evolutionary Art Applications of Evolutionary Computing, 528–537 (2009)CrossRefGoogle Scholar
  12. 12.
    McCormack, J.: Evolving Sonic Ecosystems. Kybercetes 32(1/2), 184–202 (2003)CrossRefGoogle Scholar
  13. 13.
    Goel, A.: Design, Analogy, and Creativity. IEEE Expert 12(3), 62–70 (1997)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Stoll, T.: CorpusDB: Software for Analysis, Storage, and Manipulation of Sound Corpora. Ninth Artificial Intelligence and Interactive Digital Entertainment Conference (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Thomas M. Stoll
    • 1
  1. 1.Dartmouth College, Bregman Music and Audio Research StudioHanoverUSA

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