PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A graphical model for chord progressions embedded in a psychoacoustic space
Jean-Francois Paiement, Douglas Eck, Samy Bengio and David Barber
In: The 22nd International Conference on Machine Learning, 7-11 Aug 2005, Bonn, Germany.

Abstract

Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in music. In this paper, a distributed representation for chords is designed such that Euclidean distances roughly correspond to psychoacoustic dissimilarities. Parameters in the graphical models are learnt with the EM algorithm and the classical Junction Tree algorithm. Various model architectures are compared in terms of conditional out-of-sample likelihood. Both perceptual and statistical evidence show that binary trees related to meter are well suited to capture chord dependencies.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1139
Deposited By:Jean-Francois Paiement
Deposited On:28 October 2005