LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature
Abstract
Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93,681 musical loops extracted from the DadaGP dataset, we are able to steer its generative output towards generating 3x as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool.
Cite
@article{arxiv.2303.01665,
title = {LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature},
author = {Sara Adkins and Pedro Sarmento and Mathieu Barthet},
journal= {arXiv preprint arXiv:2303.01665},
year = {2023}
}
Comments
The Version of Record of this contribution is published in Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2023