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Despite their impressive offline results, deep learning models for symbolicmusic generation are not widely used in live performances due to a deficit ofmusically meaningful control parameters and a lack of structured musical formin their outputs. To address these issues we introduce LooperGP, a method forsteering a Transformer-XL model towards generating loopable musical phrases ofa specified number of bars and time signature, enabling a tool for live codingperformances. We show that by training LooperGP on a dataset of 93,681 musicalloops extracted from the DadaGP dataset, we are able to steer its generativeoutput towards generating 3x as many loopable phrases as our baseline. In asubjective listening test conducted by 31 participants, LooperGP loops achievedpositive median ratings in originality, musical coherence and loop smoothness,demonstrating its potential as a performance tool.

LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature