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

