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.
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References
American Standards Association (1960). Acoustical Terminology, S1.1-1960. New York: American Standards Association.
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.
Aikake, H. (1977). On entropy maximization. In P. R. Krishniah (Ed.), Applications of statistics (pp. 27–41). Amsterdam: North-Holland.
Bentler, P. M., & Weeks, D. G. (1978). Restricted multidimensional scaling methods. Journal of Mathematical Psychology, 17, 138–151.
Bockenholt, U., & Bockenholt, I. (1990). Modeling individual differences in unfolding preference data: A restricted latent class approach. Applied Psychological Measurement, 14, 257–269.
Bogdozan, H. (1987). Model selection and Aikake's information criterion (AIC): The general theory and its analytic extensions. Psychometrika, 52, 345–370.
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.
Chowning, J. M. (1973). The synthesis of complex audio spectra by means of frequency modulation. Journal of the Audio Engineering Society, 21, 526–534.
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.
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.
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.
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.
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.
De Soete, G., & De Sarbo, W. (1991). A latent class probit model for analyzing pick any n data. Journal of Classification, 8, 45–63.
De Soete, G., & Heiser, W. J. (1993). A latent class unfolding model for analyzing single stimulus preference ratings. Psychometrika, 58, 545–565.
De Soete, G., & Winsberg, S. (1993). A Thurstonian pairwise choice model with univariate and multivariate spline transformations. Psychometrika, 58, 233–256.
Donnadieu, S., McAdams, S., Winsberg, S. (1994). Caracterisation du timbre des sons complexes. I: Analyse multidimensionnalle. Journal de Physique 4(C5), 593–596.
Ehresman, D., & Wessel, D. L. (1978). Perception of timbral analogies. Rapports IRCAM, 13, Paris: IRCAM.
Formann, A. K. (1989). Constrained latent class models: Some further applications. British Journal of Mathematical Psychology, 42, 37–54.
Gower, J. C. (1966). Some distance properties of latent root and vector methods using multivariate analysis. Biometrika, 53, 325–338.
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.
Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61, 1270–1277.
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.
Hope, A. C. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society, Series B, 30, 582–598.
Iverson, P., & Krumhansl, C. L. (1993). Isolating the dynamic attributes of musical timbre. Journal of the Acoustical Society of America, 94, 2595–2603.
Kendall, R. A., & Carterette, E. C. (1991). Perceptual scaling of simultaneous wind instrument timbres. Music Perception, 8, 369–404.
Krimphoff, J. (1993). Analyse acoustique et perception du timbre. Unpublished DEA thesis. Université du Maine, Le Mans, France.
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.
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).
Kruskal, J. B. (1964a). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.
Kruskal, J. B. (1964b). Non-metric multidimensional scaling: a numerical method. Psychometrika, 29, 115–129.
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.
McAdams, S., & Cunibile, J-C. (1992). Perception of timbral analogies. Philosophical Transactions of the Royal Society, London, Series B, 336, 383–389.
McLaughlin, G. J., & Basford, K. E. (1988). Mixture models. New York: Marcel Dekker.
Miller, J. R., & Carterette, E. C. (1975). Perceptual space for musical structures. Journal of the Acoustical Society of America, 58, 711–720.
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.
Plomp, R. (1976). Aspects of tone sensation. A psychophysical study. London: Academic Press.
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.
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.
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.
Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling. Psychometrika, 42, 241–266.
Sattath, S., & Tversky, A. (1977). Additive similarity trees. Psychometrika, 42, 319–345.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
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.
Shepard, R. N. (1962a). The analysis of proximities: Multidimensional scaling with an unknown distance function. Part I. Psychometrika, 27, 125–140.
Shepard, R. N. (1962b). The analysis of proximities: Multidimensional scaling with an unknown distance function. Part II. Psychometrika, 27, 219–246.
Shepard, R. N. (1982). Structural representations of musical pitch. In D. Deutsch (Ed.), The psychology of music (pp. 344–390). New York: Academic Press.
Takane, Y., & Sergent, J. (1983). Multidimensional scaling models for reaction times and some different judgments. Psychometrika, 48, 329–424.
Torgerson, W. S. (1958). Theory and methods of scaling. New York: Wiley.
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
Wedin, L., & Goude, G. (1972). Dimension analysis of the perception of instrumental timbre. Scandinavian Journal of Psychology, 13, 228–240.
Wessel, D. L. (1979). Timbre space as a musical control structure. Computer Music Journal, 3(2), 45–52.
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.
Winsberg, S., & Carroll, J. D. (1989a). A quasi-nonmetric method for multidimensional scaling via an extended Euclidean model. Psychometrika, 54, 217–229.
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.
Winsberg, S., & De Soete, G. (1993). A latent class approach to fitting the weighted Euclidean model, CLASCAL. Psychometrika, 58, 315–330.
Zwicker, E., & Scharf, B. (1965). A model of loudness summation. Psychological Review, 72, 3–26.
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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
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DOI: https://doi.org/10.1007/BF00419633