A Bayesian Network for Real-Time Musical Accompaniment

NeurIPS 2001 pp. 1433-1439

/neurips/2001/raphael2001neurips-bayesian/

Abstract

We describe a computer system that provides a real-time musi(cid:173) cal accompaniment for a live soloist in a piece of non-improvised music for soloist and accompaniment. A Bayesian network is devel(cid:173) oped 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 con(cid:173) structed using the rhythmic information contained in the musical score. The network is then trained to capture the musical interpre(cid:173) tations of the soloist and accompanist in an off-line rehearsal phase. During live accompaniment the learned distribution of the network is combined with a real-time analysis of the soloist's acoustic sig(cid:173) nal, performed with a hidden Markov model, to generate a musi(cid:173) cally principled accompaniment that respects all available sources of knowledge. A live demonstration will be provided.

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Text

Raphael. "A Bayesian Network for Real-Time Musical Accompaniment." Neural Information Processing Systems, 2001.

Markdown

[Raphael. "A Bayesian Network for Real-Time Musical Accompaniment." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/raphael2001neurips-bayesian/)

BibTeX

@inproceedings{raphael2001neurips-bayesian,
  title     = {{A Bayesian Network for Real-Time Musical Accompaniment}},
  author    = {Raphael, Christopher},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {1433-1439},
  url       = {https://mlanthology.org/neurips/2001/raphael2001neurips-bayesian/}
}