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A Bayesian Network for Real-Time Musical Accompaniment (2001) [8 citations — 1 self]

by Christopher Raphael
Advances in Neural Information Processing Systems, NIPS 14
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Abstract:

We describe a computer system that provides a real-time musical accompaniment for a live soloist in a piece of non-improvised music for soloist and accompaniment. A Bayesian network is developed that represents the joint distribution on the times at which the solo and accompaniment notes are played, relating the two parts through a layer of hidden variables. The network is first constructed using the rhythmic information contained in the musical score. The network is then trained to capture the musical interpretations of the soloist and accompanist in an off-line rehearsal phase.

Citations

420 Probabilistic Networks and Expert Systems – Cowell, Dawid, et al. - 1999
164 Bayesian analysis in expert systems – Spiegelhalter, Dawid, et al. - 1993
150 The EM algorithm for graphical association models with missing data – Lauritzen - 1995
114 Propagation of Probabilities, Means and Variances in Mixed Graphical Association Models – Lauritzen - 1992
61 Automatic segmentation of acoustic musical signals using hidden Markov models – Raphael - 1999
40 Stable Local Computation with Conditional Gaussian Distributions – Lauritzen, Jensen
18 A Probabilistic Expert System for Automatic Musical Accompaniment – Raphael
5 Synthesizing musical accompaniments with Bayesian belief networks – Raphael - 2001
1 Can the Computer Learn to Play Expressively – Raphael - 2001