Transformer Neural Networks for Automated Rhythm Generation

Thomas Nuttall, Behzad Haki, and Sergi Jorda

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract:

Recent applications of Transformer neural networks in the field of music have demonstrated their ability to effectively capture and emulate long-term dependencies characteristic of human notions of musicality and creative merit. We propose a novel approach to automated symbolic rhythm generation, where a Transformer-XL model trained on the Magenta Groove MIDI Dataset is used for the tasks of sequence generation and continuation. Hundreds of generations are evaluated using blind-listening tests to determine the extent to which the aspects of rhythm we understand to be valuable are learnt and reproduced. Our model is able to achieve a standard of rhythmic production comparable to human playing across arbitrarily long time periods and multiple playing styles.

Citation:

Thomas Nuttall, Behzad Haki, and Sergi Jorda. 2021. Transformer Neural Networks for Automated Rhythm Generation. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.21428/92fbeb44.fe9a0d82

BibTeX Entry:

  @inproceedings{NIME21_33,
 abstract = {Recent applications of Transformer neural networks in the field of music have demonstrated their ability to effectively capture and emulate long-term dependencies characteristic of human notions of musicality and creative merit. We propose a novel approach to automated symbolic rhythm generation, where a Transformer-XL model trained on the Magenta Groove MIDI Dataset is used for the tasks of sequence generation and continuation. Hundreds of generations are evaluated using blind-listening tests to determine the extent to which the aspects of rhythm we understand to be valuable are learnt and reproduced. Our model is able to achieve a standard of rhythmic production comparable to human playing across arbitrarily long time periods and multiple playing styles.},
 address = {Shanghai, China},
 articleno = {33},
 author = {Nuttall, Thomas and Haki, Behzad and Jorda, Sergi},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.21428/92fbeb44.fe9a0d82},
 issn = {2220-4806},
 month = {June},
 presentation-video = {https://youtu.be/Ul9s8qSMUgU},
 title = {Transformer Neural Networks for Automated Rhythm Generation},
 url = {https://nime.pubpub.org/pub/8947fhly},
 year = {2021}
}