Can the Computer Learn to Play Music Expressively?

Christopher Raphael
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:251-258, 2001.

Abstract

A computer system is described that provides a real-time musical accompaniment for a live soloist in a piece of non-improvised music. A Bayesian belief network is developed that represents the joint distribution on the times at which the solo and accompaniment notes are played as well as many hidden variables. The network models several important sources of information including the information contained in the score and the rhythmic interpretations of the soloist and accompaniment which are learned from examples. The network is used to provide a computationally efficient decision-making engine that utilizes all available information while producing a flexible and musical accompaniment.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR3-raphael01a, title = {Can the Computer Learn to Play Music Expressively?}, author = {Raphael, Christopher}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {251--258}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/raphael01a/raphael01a.pdf}, url = {https://proceedings.mlr.press/r3/raphael01a.html}, abstract = {A computer system is described that provides a real-time musical accompaniment for a live soloist in a piece of non-improvised music. A Bayesian belief network is developed that represents the joint distribution on the times at which the solo and accompaniment notes are played as well as many hidden variables. The network models several important sources of information including the information contained in the score and the rhythmic interpretations of the soloist and accompaniment which are learned from examples. The network is used to provide a computationally efficient decision-making engine that utilizes all available information while producing a flexible and musical accompaniment.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Can the Computer Learn to Play Music Expressively? %A Christopher Raphael %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-raphael01a %I PMLR %P 251--258 %U https://proceedings.mlr.press/r3/raphael01a.html %V R3 %X A computer system is described that provides a real-time musical accompaniment for a live soloist in a piece of non-improvised music. A Bayesian belief network is developed that represents the joint distribution on the times at which the solo and accompaniment notes are played as well as many hidden variables. The network models several important sources of information including the information contained in the score and the rhythmic interpretations of the soloist and accompaniment which are learned from examples. The network is used to provide a computationally efficient decision-making engine that utilizes all available information while producing a flexible and musical accompaniment. %Z Reissued by PMLR on 31 March 2021.
APA
Raphael, C.. (2001). Can the Computer Learn to Play Music Expressively?. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:251-258 Available from https://proceedings.mlr.press/r3/raphael01a.html. Reissued by PMLR on 31 March 2021.

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