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

  • John Ashley Burgoyne
  • Stephen McAdams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4969)


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.


Latent Classis Minimum Span Tree Spectral Shape Spectral Envelope Slight Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • John Ashley Burgoyne
    • 1
  • Stephen McAdams
    • 1
  1. 1.Centre for Interdisciplinary Research in Music and Media TechnologySchulich School of Music of McGill UniversityMontralCanada

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