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}
}