Skip to main content
Log in

Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes

  • Original Article
  • Published:
Psychological Research Aims and scope Submit manuscript

Abstract

To study the perceptual structure of musical timbre and the effects of musical training, timbral dissimilarities of synthesized instrument sounds were rated by professional musicians, amateur musicians, and nonmusicians. The data were analyzed with an extended version of the multidimensional scaling algorithm CLASCAL (Winsberg & De Soete, 1993), which estimates the number of latent classes of subjects, the coordinates of each timbre on common Euclidean dimensions, a specificity value of unique attributes for each timbre, and a separate weight for each latent class on each of the common dimensions and the set of specificities. Five latent classes were found for a three-dimensional spatial model with specificities. Common dimensions were quantified psychophysically in terms of log-rise time, spectral centroid, and degree of spectral variation. The results further suggest that musical timbres possess specific attributes not accounted for by these shared perceptual dimensions. Weight patterns indicate that perceptual salience of dimensions and specificities varied across classes. A comparison of class structure with biographical factors associated with degree of musical training and activity was not clearly related to the class structure, though musicians gave more precise and coherent judgments than did nonmusicians or amateurs. The model with latent classes and specificities gave a better fit to the data and made the acoustic correlates of the common dimensions more interpretable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • American Standards Association (1960). Acoustical Terminology, S1.1-1960. New York: American Standards Association.

    Google Scholar 

  • Aitken, M., Andersen, D., & Hinde, J. (1981). Statistical model of data on teaching styles. Journal of the Royal Statistical Society Series A, 144, 419–461.

    Google Scholar 

  • Aikake, H. (1977). On entropy maximization. In P. R. Krishniah (Ed.), Applications of statistics (pp. 27–41). Amsterdam: North-Holland.

    Google Scholar 

  • Bentler, P. M., & Weeks, D. G. (1978). Restricted multidimensional scaling methods. Journal of Mathematical Psychology, 17, 138–151.

    Google Scholar 

  • Bockenholt, U., & Bockenholt, I. (1990). Modeling individual differences in unfolding preference data: A restricted latent class approach. Applied Psychological Measurement, 14, 257–269.

    Google Scholar 

  • Bogdozan, H. (1987). Model selection and Aikake's information criterion (AIC): The general theory and its analytic extensions. Psychometrika, 52, 345–370.

    Google Scholar 

  • Carroll, J. D., & Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika, 35, 283–319.

    Google Scholar 

  • Chowning, J. M. (1973). The synthesis of complex audio spectra by means of frequency modulation. Journal of the Audio Engineering Society, 21, 526–534.

    Google Scholar 

  • Dempster, A. P., Laird, N. M., & Rubin, D. R. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1–38.

    Google Scholar 

  • De Leeuw, J., & Heiser, W. (1980). Multidimensional scaling with restrictions on the configuration. In P. R. Krishniah (Ed.), Multivariate analysis, vol. 5 (pp. 501–522). Amsterdam: North Holland.

    Google Scholar 

  • De Sarbo, W. J., Howard, D. J., & Jededi, K. (1991). MULTICLUS: A new method for simultaneously performing multidimensional scaling and cluster analysis. Psychometrika, 56, 121–136.

    Google Scholar 

  • De Soete, G. (1990). A latent class approach to modeling pairwise preferential choice data. In M. Schader & W. Gaul (Eds.), Knowledge, data, and computer-assisted decisions (pp. 103–113). Berlin: Springer-Verlag.

    Google Scholar 

  • De Soete, G., Carroll, J. D., & Chaturvedi, A. D. (1993). A modified CANDECOMP method for fitting the extended INDSCAL model. Journal of Classification, 10, 75–90.

    Google Scholar 

  • De Soete, G., & De Sarbo, W. (1991). A latent class probit model for analyzing pick any n data. Journal of Classification, 8, 45–63.

    Google Scholar 

  • De Soete, G., & Heiser, W. J. (1993). A latent class unfolding model for analyzing single stimulus preference ratings. Psychometrika, 58, 545–565.

    Google Scholar 

  • De Soete, G., & Winsberg, S. (1993). A Thurstonian pairwise choice model with univariate and multivariate spline transformations. Psychometrika, 58, 233–256.

    Google Scholar 

  • Donnadieu, S., McAdams, S., Winsberg, S. (1994). Caracterisation du timbre des sons complexes. I: Analyse multidimensionnalle. Journal de Physique 4(C5), 593–596.

    Google Scholar 

  • Ehresman, D., & Wessel, D. L. (1978). Perception of timbral analogies. Rapports IRCAM, 13, Paris: IRCAM.

    Google Scholar 

  • Formann, A. K. (1989). Constrained latent class models: Some further applications. British Journal of Mathematical Psychology, 42, 37–54.

    Google Scholar 

  • Gower, J. C. (1966). Some distance properties of latent root and vector methods using multivariate analysis. Biometrika, 53, 325–338.

    Google Scholar 

  • Grey, J. M. (1975). An exploration of musical timbre. Unpublished Ph.D. dissertation, Stanford University, Stanford, CA. Stanford University, Dept. of Music Report STAN-M-2.

    Google Scholar 

  • Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61, 1270–1277.

    Google Scholar 

  • Grey, J. M., & Gordon, J. W. (1978). Perceptual effects of spectral modifications on musical timbres. Journal of the Acoustical Society of America, 63, 1493–1500.

    Google Scholar 

  • Hope, A. C. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society, Series B, 30, 582–598.

    Google Scholar 

  • Iverson, P., & Krumhansl, C. L. (1993). Isolating the dynamic attributes of musical timbre. Journal of the Acoustical Society of America, 94, 2595–2603.

    Google Scholar 

  • Kendall, R. A., & Carterette, E. C. (1991). Perceptual scaling of simultaneous wind instrument timbres. Music Perception, 8, 369–404.

    Google Scholar 

  • Krimphoff, J. (1993). Analyse acoustique et perception du timbre. Unpublished DEA thesis. Université du Maine, Le Mans, France.

    Google Scholar 

  • Krimphoff, J., McAdams, S., & Winsberg, S. (1994). Caractérisation du timbre des sons complexes. II: Analyses acoustiques et quantification psychophysique. Journal de Physique, 4(C5), 625–628.

    Google Scholar 

  • Krumhansl, C. L. (1989). Why is musical timbre so hard to understand? In S. Nielzén & O. Olsson (Eds.), Structure and perception of electroacoustic sound and music (pp. 43–53). Amsterdam: Elsevier (Excerpta Medica 846).

    Google Scholar 

  • Kruskal, J. B. (1964a). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.

    Google Scholar 

  • Kruskal, J. B. (1964b). Non-metric multidimensional scaling: a numerical method. Psychometrika, 29, 115–129.

    Google Scholar 

  • McAdams, S. (1993). Recognition of auditory sources and events. In S. McAdams & E. Bigand (Eds.), Thinking in sound: The cognitive psychology of human audition (pp. 146–198). Oxford: Oxford University Press.

    Google Scholar 

  • McAdams, S., & Cunibile, J-C. (1992). Perception of timbral analogies. Philosophical Transactions of the Royal Society, London, Series B, 336, 383–389.

    Google Scholar 

  • McLaughlin, G. J., & Basford, K. E. (1988). Mixture models. New York: Marcel Dekker.

    Google Scholar 

  • Miller, J. R., & Carterette, E. C. (1975). Perceptual space for musical structures. Journal of the Acoustical Society of America, 58, 711–720.

    Google Scholar 

  • Plomp, R. (1970). Timbre as a multidimensional attribute of complex tones. In R. Plomp & G. F. Smoorenburg (Eds.), Frequency analysis and periodicity detection in hearing (pp. 397–414). Leiden: Sijthoff.

    Google Scholar 

  • Plomp, R. (1976). Aspects of tone sensation. A psychophysical study. London: Academic Press.

    Google Scholar 

  • Plomp, R., Pols, L. C. W., & van de Geer, J. P. (1967). Dimensional analysis of vowel spectra. Journal of the Acoustical Society of America, 41, 707–712.

    Google Scholar 

  • Plomp, R., & Steenecken, H. J. M. (1969). Effect of phase on the timbre of complex tones. Journal of the Acoustical Society of America, 46, 409–421.

    Google Scholar 

  • Pols, L. C. W., van der Kamp, L. J. T., & Plomp, R. (1969). Perceptual and physical space of vowel sounds. Journal of the Acoustical Society of America, 46, 458–467.

    Google Scholar 

  • Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling. Psychometrika, 42, 241–266.

    Google Scholar 

  • Sattath, S., & Tversky, A. (1977). Additive similarity trees. Psychometrika, 42, 319–345.

    Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.

    Google Scholar 

  • Serafini, S. (1993). Timbre perception of cultural insiders: A case study with Javanese gamelan instruments. Unpublished Master's thesis, University of British Columbia, Vancouver, BC.

    Google Scholar 

  • Shepard, R. N. (1962a). The analysis of proximities: Multidimensional scaling with an unknown distance function. Part I. Psychometrika, 27, 125–140.

    Google Scholar 

  • Shepard, R. N. (1962b). The analysis of proximities: Multidimensional scaling with an unknown distance function. Part II. Psychometrika, 27, 219–246.

    Google Scholar 

  • Shepard, R. N. (1982). Structural representations of musical pitch. In D. Deutsch (Ed.), The psychology of music (pp. 344–390). New York: Academic Press.

    Google Scholar 

  • Takane, Y., & Sergent, J. (1983). Multidimensional scaling models for reaction times and some different judgments. Psychometrika, 48, 329–424.

    Google Scholar 

  • Torgerson, W. S. (1958). Theory and methods of scaling. New York: Wiley.

    Google Scholar 

  • Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Wedin, L., & Goude, G. (1972). Dimension analysis of the perception of instrumental timbre. Scandinavian Journal of Psychology, 13, 228–240.

    Google Scholar 

  • Wessel, D. L. (1979). Timbre space as a musical control structure. Computer Music Journal, 3(2), 45–52.

    Google Scholar 

  • Wessel, D. L., Bristow, D., & Settel, Z. (1987). Control of phrasing and articulation in synthesis. Proceedings of the 1987 International Computer Music Conference (pp. 108–116). Computer Music Association, San Francisco.

    Google Scholar 

  • Winsberg, S., & Carroll, J. D. (1989a). A quasi-nonmetric method for multidimensional scaling via an extended Euclidean model. Psychometrika, 54, 217–229.

    Google Scholar 

  • Winsberg, S., & Carroll, J. D. (1989b). A quasi-nonmetric method for multidimensional scaling of multiway data via a restricted case of an extended INDSCAL model. In R. Coppi & S. Bolasco (Eds.), Multiway data analysis (pp. 405–414). Amsterdam: North-Holland.

    Google Scholar 

  • Winsberg, S., & De Soete, G. (1993). A latent class approach to fitting the weighted Euclidean model, CLASCAL. Psychometrika, 58, 315–330.

    Google Scholar 

  • Zwicker, E., & Scharf, B. (1965). A model of loudness summation. Psychological Review, 72, 3–26.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

McAdams, S., Winsberg, S., Donnadieu, S. et al. Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes. Psychol. Res 58, 177–192 (1995). https://doi.org/10.1007/BF00419633

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00419633

Keywords

Navigation