Abstract
Seeking to identify the constituent parts of the multidimensional auditory attribute that musicians know as timbre, music psychologists have made extensive use of multidimensional scaling (mds), a statistical technique for visualising the geometric spaces implied by perceived dissimilarity. mds is also well known in the machine learning community, where it is used as a basic technique for dimensionality reduction. We adapt a nonlinear variant of mds that is popular in machine learning, Isomap, for use in analysing psychological data and re-analyse three earlier experiments on human perception of timbre. Isomap is designed to eliminate undesirable nonlinearities in the input data in order to reduce the overall dimensionality; our results show that it succeeds in these goals for timbre spaces, compressing the output onto well-known dimensions of timbre and highlighting the challenges inherent in quantifying differences in spectral shape.
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Burgoyne, J.A., McAdams, S. (2008). A Meta-analysis of Timbre Perception Using Nonlinear Extensions to CLASCAL. In: Kronland-Martinet, R., Ystad, S., Jensen, K. (eds) Computer Music Modeling and Retrieval. Sense of Sounds. CMMR 2007. Lecture Notes in Computer Science, vol 4969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85035-9_12
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DOI: https://doi.org/10.1007/978-3-540-85035-9_12
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