Semiotics of Sounds Evoking Motions: Categorization and Acoustic Features

  • Adrien Merer
  • Sølvi Ystad
  • Richard Kronland-Martinet
  • Mitsuko Aramaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4969)


The current study is part of a larger project aiming at offering intuitive mappings of control parameters piloting synthesis models by semantic descriptions of sounds, i.e. simple verbal labels related to various feelings, emotions, gestures or motions. Hence, this work is directly related to the general problem of semiotics of sounds. We here put a special interest in sounds evoking different perceived motions. In this paper, the experimental design of the listening tests is described and the results obtained from behavioural data are discussed. Then a set of signal descriptors is compared to categories using feature selection methods. A special interest is given to applications for sound synthesis.


sound semiotics motion categorization sound synthesis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aramaki, M., Bailleres, H., Brancheriau, L., Kronland-Martinet, R., Ystad, S.: Sound Quality Assessment of Wood for Xylophone Bars. Journal of the Acoustical Society of America 121(4), 2407–2420 (2007)CrossRefGoogle Scholar
  2. 2.
    Bigand, E.: Multidimensional scaling of emotional responses to music: The effect of musical expertise and of the duration of the excerpts. Cognition and emotion 19(8), 1113–1139 (2005)CrossRefGoogle Scholar
  3. 3.
    Blum, A., Langley, P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245–271 (1997)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Defreville, P., Roy, B., Pachet, F.: Automatic Recognition of Urban Sound Sources. In: Proceedings of the 120th AES Conference (2006)Google Scholar
  5. 5.
    Eitan, Z., Granot, R.Y.: How music moves: Musical parameters and listeners’ images of motion. Music perception 23(3), 221–247 (2006)CrossRefGoogle Scholar
  6. 6.
    Gaver, W.W.: What in the world do we hear? An ecological approach to auditory event perception. Ecological Psychology 5(1), 1–29 (1993)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Gibson, J.J.: The ecological approach to visual perception. Houghton Mifflin, Boston (1979)Google Scholar
  8. 8.
    Grey, J.M., Gordon, J.W.: Perceptual effects of spectral modifications on musical timbres. The Journal of the Acoustical Society of America 63(5), 1493–1500 (1978)CrossRefGoogle Scholar
  9. 9.
    Guastavino, C.: Categorization of environmental sounds. Canadian Journal of Experimental Psychology 61(1), 54–63 (2007)Google Scholar
  10. 10.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)MATHCrossRefGoogle Scholar
  11. 11.
    Hall, M.: Correlation-based feature selection of discrete and numeric class machine learning. In: Proceedings of the International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  12. 12.
    Herrera, P., Yeterian, A., Gouyon, F.: Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques. In: Proceedings of Second International Conference on Music and Artificial Intelligence, Edinburgh, Scotland (2002)Google Scholar
  13. 13.
    Jekosch, U.: Assigning Meaning to Sounds - Semiotics in the Context of Product-Sound Design. In: Blauert, J. (ed.) Communication Acoustics. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Kawai, K., Kojima, K., Hirate, K., Yasuoka, M.: Personal evaluation structure of environmental sounds: experiment of subjective evaluation using subjects own terms. Journal of sound and vibrations (2004)Google Scholar
  15. 15.
    Lufti, A., Wang, W.: Correlational analysis of acoustic cues for the discrimination of auditory motion. Journal of the Acoustical Society of America 106(2) (August 1999)Google Scholar
  16. 16.
    McAdams, S.: Recognition of sound sources and events. In: McAdams, S., Bigand, E. (eds.) Thinking in sound The cognitive psychology of human audition, pp. 146–198. Oxford University Press, Oxford (1993)Google Scholar
  17. 17.
    Kim, H.G., Moreau, N., Sikora, T.: MPEG-7 Audio and Beyond: audio content indexing and retrieval. Wiley, Chichester (2005)Google Scholar
  18. 18.
    Peeters, G.: A large set audio features for sound description (similarity and classification) in the CUIDADO project. In: IRCAM (2004)Google Scholar
  19. 19.
    Portnoff, M.R.: Implementation of the digital phase vocoder using the fast Fourier transform. IEEE Transactions on acoustics, speech and signal processing 24(3) (1976)Google Scholar
  20. 20.
    Pachet, F., Roy, P.: Exploring billions of audio features. In: Proceedings of CBMI 2007 (2007)Google Scholar
  21. 21.
    Schaeffer, P.: Traité des objets musicaux Editions du seuil (1966)Google Scholar
  22. 22.
    Widmer, G., Dixon, S., Knees, P., Pampalk, E., Pohle, E.: From Sound to ”Sense” via Feature Extraction and Machine Learning: Deriving High-level Descriptors for Characterising Music. In: Polotti, P., Rocchesso, D. (eds.) Sound to Sense, Sense to Sound: A State-of-the-Art (2007)Google Scholar
  23. 23.
    Ystad, S., Kronland-Martinet, R., Schön, D., Besson, M.: Vers une approche acoustique et cognitive de la sémiotique des objets sonores, UST: Théorie et Applications (2005)Google Scholar
  24. 24.
    Zwicker, E., Fastl, H.: Psycho-acoustics, facts and models. Springer, Heidelberg (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adrien Merer
    • 1
  • Sølvi Ystad
    • 1
  • Richard Kronland-Martinet
    • 1
  • Mitsuko Aramaki
    • 2
    • 3
  1. 1.CNRS - Laboratoire de Mécanique et d’Acoustique MarseilleFrance
  2. 2.CNRS - Institut de Neurosciences Cognitives de la Méditerranée MarseilleFrance
  3. 3.Université Aix-MarseilleMarseilleFrance

Personalised recommendations