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Transformed Sound as Personal Construct

Published online by Cambridge University Press:  24 November 2017

Peter Plessas*
Affiliation:
University of Music and Performing Arts Graz, Leonhardstrasse 15, 8010 Graz, Austria

Abstract

To better understand the function of sound within electronic music it is inevitable to consider the way composers and performers talk about such sound and its different qualities. An attempt to classify sound is a logical consequence of the desire to orient oneself in a vast field of possibilities. It remains difficult, however, to relate such a classification to the intuitive way we talk about sound. Heavily influenced by our individual upbringing, cultural and musical environment and, perhaps most strongly, by our peers in the creation and performance of music, words verbalised in relation to sound and music invite a systematic investigation. This article exemplifies such an investigation, attempting the elicitation and structuring of a vocabulary from eight musically trained individuals in a two-stage experiment. The words examined here are concerned with sounds created by transformation of acoustic sources through electronic processing methods.

Type
Articles
Copyright
© Cambridge University Press 2017 

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Footnotes

The author would like to thank all participants of the experiment as well as Marij van Gorkom, Gerhard Eckel, Florian Hollerweger, Stephen McAdams and the two anonymous reviewers. This work was supported by the University of Music and Performing Arts, Graz, the Provincial Government of Styria and the Austrian Federal Ministry of Science, Research and Economy.

References

Adriaensen, F. 2011. Loudness Measurement According to EBU R-128. Proceedings of the Linux Audio Conference 2011. Maynooth, Ireland, 6–8 May.Google Scholar
Atsushi, M. and Martens, W. L. 2005. Constructing Individual and Group Timbre Spaces for Sharpness-matched Distorted Guitar Timbres. Audio Engineering Society (AES), 119th Audio Engineering Society Convention. New York, 7–10 October.Google Scholar
Avni, A. 2010. Spaciousness of Sound Fields Captured by Spherical Microphone Arrays. Doctoral dissertation, Ben-Gurion University of the Negev.Google Scholar
Berg, J. and Rumsey, F. 1999. Spatial Attribute Identification and Scaling by Repertory Grid Technique and Other Methods. Proceedings of the AES 16th International Conference on Spatial Sound Reproduction. Rovaniemi, Finland, 10–12 April.Google Scholar
Dahinden, R. 2009. action for jackson. Roland Dahinden, music score.Google Scholar
Faure, A., McAdams, S. and Nosulenko, V. 1996. Verbal Correlates of Perceptual Dimensions of Timbre. 4th International Conference on Music Perception and Cognition. Montreal, Canada.Google Scholar
Fransella, F., Bell, R. and Bannister, D. 2004. A Manual for Repertory Grid Technique. Chichester: John Wiley.Google Scholar
Greenacre, M. J. 2010. Biplots in Practice. Bilbao, Spain: Fundacion BBVA.Google Scholar
Grill, T., Flexer, A. and Cunningham, S. 2011. Identification of Perceptual Qualities in Textural Sounds Using the Repertory Grid Method. Proceedings of the 6th Audio Mostly Conference: A Conference on Interaction with Sound. Coimbra, Portugal, 7–9 September.Google Scholar
Heckmann, M. 2016. OpenRepGrid. https://markheckmann.wordpress.com/.Google Scholar
Helmholtz, H. L. 1863/2009. On the Sensations of Tone as a Physiological Basis for the Theory of Music. Trans. A. J. Ellis. Cambridge: Cambridge University Press.Google Scholar
Kelly, G. A. 1970/2003. A Brief Introduction to Personal Construct Theory. In F. Fransella (ed.) International Handbook of Personal Construct Psychology. Chichester: John Wiley.Google Scholar
Kendall, R. A. and Carterette, E. C. 1993a. Verbal Attributes of Simultaneous Wind Instrument Timbres: I. von Bismarck’s Adjectives. Music Perception: An Interdisciplinary Journal 10(4): 445467.Google Scholar
Kendall, R. A. and Carterette, E. C. 1993b. Verbal Attributes of Simultaneous Wind Instrument Timbres: II. Adjectives Induced from Piston’s ‘Orchestration’. Music Perception: An Interdisciplinary Journal 10(4): 469501.Google Scholar
Kjeldsen, A. D. 1998. The Measurement of Personal Preferences by Repertory Grid Technique. Audio Engineering Society Convention 104. Copenhagen, Denmark, 1 May.Google Scholar
Muzzulini, D. 2006. Genealogie der Klangfarbe, vol. 5. Bern: Peter Lang.Google Scholar
Octave community 2016. Octave. www.gnu.org/software/octave/.Google Scholar
Puckette, M. 1996. Pure Data: Another Integrated Computer Music Environment. Proceedings of the Second Intercollege Computer Music Concerts. Tachikawa, Japan.Google Scholar
Core Team, R 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.Google Scholar
Schaeffer, P. 1966. Traité des objets musicaux. Paris: Éditions du Seuil.Google Scholar
Siedenburg, K. 2016. Perspectives of Memory for Musical Timbre. Doctoral dissertation, McGill University.Google Scholar
Smalley, D. 1997. Spectromorphology: Explaining Sound-shapes. Organised Sound 2(2): 107126.CrossRefGoogle Scholar
Stables, R., De Man, B., Enderby, S., Reiss, J. D., Fazekas, G. and Wilmering, T. 2016. Semantic Description of Timbral Transformations in Music Production. Proceedings of the 2016 ACM on Multimedia Conference. Amsterdam, 15–19 October.Google Scholar
Verfaille, V., Guastavino, C. and Traube, C. 2006. An Interdisciplinary Approach to Audio Effect Classification. Proceedings of the 9th International Conference on Digital Audio Effects (DAFx-06). Montreal, Canada, 18–20 September.Google Scholar
von Bismarck, G. 1974. Timbre of Steady Sounds: A Factorial Investigation of its Verbal Attributes. Acta Acustica united with Acustica 30(3): 146159.Google Scholar
Zacharakis, A., Pastiadis, K., Reiss, J. D. and Papadelis, G. 2012. Analysis of Musical Timbre Semantics through Metric and Non-metric Data Reduction Techniques. Proceedings of the 12th International Conference on Music Perception and Cognition (ICMPC12) and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 08). Thessaloniki, Greece, 23–8 July.Google Scholar
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