A Meta-analysis of Timbre Perception Using Nonlinear Extensions to CLASCAL

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.

• Type: Conference
• Year: 2007
• Where: CMMR
• Authors: John Ashley Burgoyne, Stephen McAdams
• Keywords: CMMR 2007, Machine Learning, Multidimensional Auditory Attribute, Music, Timbre Spaces