  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.
