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International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)

EvoMUSART 2023: Artificial Intelligence in Music, Sound, Art and Design pp 3–19Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13988))

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 [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.

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Notes

  1. 1.

    We focus here on loops where the exact same content is repeated, but it is worth noting that a more general definition could encompass loops where certain types of musical variations can occur across repetitions (e.g. modulation).

  2. 2.

    Link to listening test excerpts: https://drive.google.com/drive/folders/1I0MCPYjj8nXqKkmDN-d-C2ETOHJpCZyn?usp=share_link.

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Acknowledgements

This work has been partly supported by the EPSRC UKRI Centre for Doctoral Training in Artificial Intelligence and Music (Grant no. EP/S022694/1).

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Authors and Affiliations

  1. Centre for Digital Music, Queen Mary University of London, London, UK

    Sara Adkins, Pedro Sarmento & Mathieu Barthet

Authors
  1. Sara Adkins
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  2. Pedro Sarmento
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  3. Mathieu Barthet
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Corresponding author

Correspondence to Sara Adkins .

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Editors and Affiliations

  1. University of Nottingham, Nottingham, UK

    Colin Johnson

  2. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

  3. University of Coimbra, Coimbra, Portugal

    Sérgio M. Rebelo

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Adkins, S., Sarmento, P., Barthet, M. (2023). LooperGP: A Loopable Sequence Model for Live Coding Performance Using GuitarPro Tablature. In: Johnson, C., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023. Lecture Notes in Computer Science, vol 13988. Springer, Cham. https://doi.org/10.1007/978-3-031-29956-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-29956-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29955-1

  • Online ISBN: 978-3-031-29956-8

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