Perceptual scaling of synthesized musical timbres: Common dimensions,specificities, and latent subject classes |
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Authors: | Stephen McAdams Suzanne Winsberg Sophie Donnadieu Geert De Soete Jochen Krimphoff |
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Affiliation: | (1) Laboratoire de Psychologie Expérimentale (CNRS), Université René Descartes, EPHE, 28 rue Serpente, F-75006 Paris, France;(2) Institut de Recherche et de Coordination Acoustique/Musique (IRCAM), 1 Place Igor-Stravinsky, F-75004 Paris, France;(3) IRCAM, 1 Place Igor-Stravinsky, F-75004 Paris, France;(4) Laboratoire de Psychologie Experimentale (CNRS) and IRCAM, Paris, France;(5) Department of Data Analysis, University of Ghent, Henri Dunantlaan 1, B-9000 Ghent, Belgium;(6) IRCAM, Paris, France |
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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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