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Synthesising Timbres and Timbre-Changes from Adjectives/Adverbs

  • Alex Gounaropoulos
  • Colin Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

Synthesising timbres and changes to timbres from natural language descriptions is an interesting challenge for computer music. This paper describes the current state of an ongoing project which takes a machine learning approach to this problem. We discuss the challenges that are presented by this, discuss various strategies for tackling this problem, and explain some experimental work. In particular our approach is focused on the creation of a system that uses an analysis-synthesis cycle to learn and then produce such timbre changes.

Keywords

Genetic Algorithm Machine Learning Method Synthesis Parameter Synthesis Algorithm Amplitude Envelope 
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.

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References

  1. 1.
    Miranda, E.R.: An artificial intelligence approach to sound design. Computer Music Journal 19(2), 59–75 (1995)CrossRefGoogle Scholar
  2. 2.
    Seago, A., Holland, S., Mulholland, P.: A critical analysis of synthesizer user interfaces for timbre. In: Proceedings of the XVIIIrd British HCI Group Annual Conference. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Wishart, T.: On Sonic Art, 2nd edn. Harwood Academic Publishers (1996); revised by Emmerson, S., 1st edn. (1985)Google Scholar
  4. 4.
    Etherington, R., Punch, B.: SeaWave: A system for musical timbre description. Computer Music Journal 18(1), 30–39 (1994)CrossRefGoogle Scholar
  5. 5.
    Wishart, T.: Audible Design. Orpheus the Pantomime (1994)Google Scholar
  6. 6.
    Fitzgerald, R.A., Lindsay, A.T.: Tying semantic labels to computational descriptors of similar timbres. In: Proceedings of Sound and Music Computing 2004 (2004)Google Scholar
  7. 7.
    Wessel, D.M.: Timbre space as a musical control structure. Computer Music Journal 3(2) (1979)Google Scholar
  8. 8.
    McAdams, S., Winsberg, S., Donnadieu, S., de Soete, G., Krimphoff, J.: Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes. Psychological Research 58, 177–192 (1995)CrossRefGoogle Scholar
  9. 9.
    Kostek, B.: Soft Computing in Acoustics. Physica-Verlag (1999)Google Scholar
  10. 10.
    Grey, J.M.: An Exploration of Musical Timbre. PhD thesis, Stanford University, Department of Music (1975)Google Scholar
  11. 11.
    Disley, A.C., Howard, D.M.: Spectral correlates of timbral semantics relating to the pipe organ. Speech, Music and Hearing 46 (2004)Google Scholar
  12. 12.
    Johnson, C.G.: Exploring the sound-space of synthesis algorithms using interactive genetic algorithms. In: Wiggins, G.A. (ed.) Proceedings of the AISB Workshop on Artificial Intelligence and Musical Creativity, Edinburgh (1999)Google Scholar
  13. 13.
    McDermott, J., Griffith, N.J.L., O’Neill, M.: Toward user-directed evolution of sound synthesis parameters. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 517–526. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Yuen, J., Horner, A.: Hybrid sampling-wavetable synthesis with genetic algorithms. Journal of the Audio Engineering Society 45(5), 316–330 (1997)Google Scholar
  15. 15.
    Sloman, A.: Exploring design space and niche space. In: 5th Scandinavian Conference on AI. IOS Press, Amsterdam (1995)Google Scholar
  16. 16.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  17. 17.
    Mitchell, M.: An Introduction to Genetic Algorithms. Series in Complex Adaptive Systems. Bradford Books/MIT Press (1996)Google Scholar
  18. 18.
    Riionheimo, J., Välimäki, V.: Parameter estimation of a plucked string synthesis model using a genetic algorithm with perceptual fitness calculation. EURASIP Journal on Applied Signal Processing 8, 791–805 (2003)Google Scholar
  19. 19.
    Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Heidelberg (2002)MATHGoogle Scholar
  20. 20.
    Howard, D.M., Tyrrell, A.M.: Psychoacoustically informed spectrography and timbre. Organised Sound 2(2), 65–76 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alex Gounaropoulos
    • 1
  • Colin Johnson
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterburyEngland

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