English

Piano Genie

Machine Learning 2019-03-25 v2 Human-Computer Interaction Sound Audio and Speech Processing Machine Learning

Abstract

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user's input for the encoder output, and play the decoder's prediction each time the user presses a button. To improve the intuitiveness of Piano Genie's performance behavior, we impose musically meaningful constraints over the encoder's outputs.

Keywords

Cite

@article{arxiv.1810.05246,
  title  = {Piano Genie},
  author = {Chris Donahue and Ian Simon and Sander Dieleman},
  journal= {arXiv preprint arXiv:1810.05246},
  year   = {2019}
}

Comments

Published as a conference paper at ACM IUI 2019

R2 v1 2026-06-23T04:36:59.944Z