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

被引：2

作者：

Adkins, Sara ^{[1
]}

Sarmento, Pedro ^{[1
]}

Barthet, Mathieu ^{[1
]}

机构：

[1] Queen Mary Univ London, Ctr Digital Music, London, England

来源：

ARTIFICIAL INTELLIGENCE IN MUSIC, SOUND, ART AND DESIGN, EVOMUSART 2023 | 2023年 / 13988卷

基金：

英国工程与自然科学研究理事会;

关键词：

Controllable Music Generation; Sequence Models; Live Coding; Transformers; AI Music; Loops; Guitar Tabs; GENERATIVE MUSIC;

D O I：

10.1007/978-3-031-29956-8_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 [22], 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.

引用

页码：3 / 19

页数：17