Polyphonic music modeling with random fields
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Connection Science - Music, Brain, Cognition
Probabilistic melodic harmonization
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Harmonizing melody with meta-structure of piano accompaniment figure
Journal of Computer Science and Technology - Special issue on Natural Language Processing
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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.