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A Rise–Fall Temporal Asymmetry of Intensity in Composed and Improvised Electroacoustic Music

Published online by Cambridge University Press:  06 July 2010

Roger T. Dean*
Affiliation:
MARCS Auditory Laboratories, University of Western Sydney, Locked Bag 1797, Penrith DC, NSW 1797, Australia
Freya Bailes*
Affiliation:
MARCS Auditory Laboratories, University of Western Sydney, Locked Bag 1797, Penrith DC, NSW 1797, Australia

Abstract

A computational analysis of temporal patterns of acoustic intensity is applied to a series of recorded electroacoustic works in order to discern whether there are recurrent patterns in the intensity rise–fall structure. The works range from 1962 (Xenakis) to 2001 (Normandeau), and include composition and improvisation, large-scale (>25 minutes) and small-scale (<2 minutes) pieces, and acoustic, electronic and manipulated and synthesised sound. Contrary to Huron’s 1991 finding of long and gradual crescendi compared to short and abrupt decrescendi in classical music notation, it is found that in successive rise–fall events the rise phase (crescendo) is commonly shorter than the fall, and has a greater absolute rate of change of intensity than the fall. Correspondingly, crescendi occupy a smaller portion of the piece than decrescendi. When categorised into dynamic steps (such as p, mp, mf etc.), crescendi are usually less numerous than decrescendi. The results are considered from a cognitive point of view, in that this structuring may influence and thus result from listener attention and arousal patterns; and from a creator/performer point of view, in that embodied or virtualised effort may be key to the impact of an electroacoustic piece.

Type
Articles
Copyright
Copyright © Cambridge University Press 2010

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References

Bailes, F., Dean, R. T. 2009a. Empirical Studies of Computer Sound. In R. T. Dean (ed.) The Oxford Handbook of Computer Music. New York: Oxford University Press.Google Scholar
Bailes, F., Dean, R. T. 2009b. Listeners Discern Affective Variation in Computer-Generated Musical Sounds. Perception 38: 13861404.CrossRefGoogle ScholarPubMed
Barnes, R., Jones, M. R. 2000. Expectancy, Attention, and Time. Cognitive Psychology 41(3): 254311.CrossRefGoogle ScholarPubMed
Boersma, P., Weenink, D. 1992–2009. Praat, v. 5.0. www.praat.org.Google Scholar
Dean, R. T. 2003. Hyperimprovisation: Computer Interactive Sound Improvisation, with CD-Rom. Madison, WI: A-R Editions.Google Scholar
Dean, R. T., Bailes, F. 2008. Is there a ‘Rise-Fall Temporal Archetype’ of Intensity in Electroacoustic Music? Canadian Acoustics 36(3): 112113.Google Scholar
Geringer, J. M. 1993. Loudness Estimations of Noise, Synthesizer and Music Excerpts by Musicians and Nonmusicians. Psychomusicology 12: 2230.CrossRefGoogle Scholar
Geringer, J. M. 1995. Continuous Loudness Judgements of Dynamics in Recorded Music Excerpts. Journal of Research in Music Education 43(1): 2235.CrossRefGoogle Scholar
Glasberg, B. R., Moore, B. C. J. 2002. A Model of Loudness Applicable to Time-Varying Sounds. Journal of the Audio Engineering Society 50(5): 331342.Google Scholar
Huron, D. 1991. The Ramp Archetype: A Study of Musical Dynamics in 14 Piano Composers. Psychology of Music 19: 3345.CrossRefGoogle Scholar
Huron, D. 1992. The Ramp Archetype and the Maintenance of Auditory Attention. Music Perception 10: 435444.CrossRefGoogle Scholar
Knudsen, E. I. 2007. Fundamental Components of Attention. Annual Review of Neuroscience 30: 5778.CrossRefGoogle ScholarPubMed
Krumhansl, C. L. 1996. A Perceptual Analysis of Mozart’s Piano Sonata K. 282: Segmentation, Tension, and Musical Ideas. Music Perception 13: 401432.CrossRefGoogle Scholar
Mathews, M. V. 1979. Perception of Crescendos as a Function of Duration. The Journal of the Acoustical Society of America 65: S123.CrossRefGoogle Scholar
Neuhoff, J. G. 1998. Perceptual Bias for Rising Tones. Nature 395: 123.CrossRefGoogle ScholarPubMed
Olsen, K. N., Stevens, C., Tardieu, J. 2007. A Perceptual Bias for Increasing Loudness: Loudness Change and its Role in Music and Mood. International Conference on Music Communication Science. Sydney: http://marcs.uws.edu.au/links/ICoMusic/Full_Paper_PDF/Olsen_Stevens_Tardieu.pdf.Google Scholar
Schubert, E. 2004. Modelling Perceived Emotion with Continuous Musical Features. Music Perception 21: 561585.CrossRefGoogle Scholar
Smith, H., Dean, R. T. 1997. Improvisation, Hypermedia and the Arts since 1945. London: Harwood Academic.Google Scholar
Zwicker, E., Fastl, H. 1999. Psychoacoustics. Berlin: Springer.CrossRefGoogle Scholar