Calliope -- A Polyphonic Music Transformer
Sound
2021-07-13 v1 Machine Learning
Audio and Speech Processing
Abstract
The polyphonic nature of music makes the application of deep learning to music modelling a challenging task. On the other hand, the Transformer architecture seems to be a good fit for this kind of data. In this work, we present Calliope, a novel autoencoder model based on Transformers for the efficient modelling of multi-track sequences of polyphonic music. The experiments show that our model is able to improve the state of the art on musical sequence reconstruction and generation, with remarkably good results especially on long sequences.
Cite
@article{arxiv.2107.05546,
title = {Calliope -- A Polyphonic Music Transformer},
author = {Andrea Valenti and Stefano Berti and Davide Bacciu},
journal= {arXiv preprint arXiv:2107.05546},
year = {2021}
}
Comments
Accepted at ESANN2021