On the Adaptability of Recurrent Neural Networks for Real-Time Jazz Improvisation Accompaniment

Jazz improvisation on a given lead sheet with chords is an interesting scenario for studying the behaviour of artificial agents when they collaborate with humans. Specifically in jazz improvisation, the role of the accompanist is crucial for reflecting the harmonic and metric characteristics of a ja...

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Published inFrontiers in artificial intelligence Vol. 3; p. 508727
Main Authors Kritsis, Kosmas, Kylafi, Theatina, Kaliakatsos-Papakostas, Maximos, Pikrakis, Aggelos, Katsouros, Vassilis
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 12.02.2021
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Summary:Jazz improvisation on a given lead sheet with chords is an interesting scenario for studying the behaviour of artificial agents when they collaborate with humans. Specifically in jazz improvisation, the role of the accompanist is crucial for reflecting the harmonic and metric characteristics of a jazz standard, while identifying in real-time the intentions of the soloist and adapt the accompanying performance parameters accordingly. This paper presents a study on a basic implementation of an artificial jazz accompanist, which provides accompanying chord voicings to a human soloist that is conditioned by the soloing input and the harmonic and metric information provided in a lead sheet chart. The model of the artificial agent includes a separate model for predicting the intentions of the human soloist, towards providing proper accompaniment to the human performer in real-time. Simple implementations of Recurrent Neural Networks are employed both for modeling the predictions of the artificial agent and for modeling the expectations of human intention. A publicly available dataset is modified with a probabilistic refinement process for including all the necessary information for the task at hand and test-case compositions on two jazz standards show the ability of the system to comply with the harmonic constraints within the chart. Furthermore, the system is indicated to be able to provide varying output with different soloing conditions, while there is no significant sacrifice of "musicality" in generated music, as shown in subjective evaluations. Some important limitations that need to be addressed for obtaining more informative results on the potential of the examined approach are also discussed.
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Reviewed by: Rocco Zaccagnino, University of Salerno, Italy
Edited by: Steven Kemper, The State University of New Jersey, United States
Tetsuro Kitahara, Nihon University, Japan
This article was submitted to Machine Learning and Artificial Intelligence, a section of the journal Frontiers in Artificial Intelligence
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2020.508727