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Building an Embodied Musicking Dataset for co-creative music-making

Vear, Craig; Poltronieri, Fabrizio; DiDonato, Balandino; Zhang, Yawen; Benerradi, Johann; Hutchinson, Simon; Turowski, Paul; Shell, Jethro; Malekmohamadi, Hossein

Authors

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CRAIG VEAR Craig.Vear@nottingham.ac.uk
Professor in Music & Computer Science

Balandino DiDonato

Yawen Zhang

Johann Benerradi

Simon Hutchinson

Paul Turowski

Jethro Shell

Hossein Malekmohamadi



Abstract

In this paper, we present our findings of the design, development and deployment of a proof-of-concept dataset that captures some of the physiological, musicological, and psychological aspects of embodied musicking. After outlining the conceptual elements of this research, we explain the design of the dataset and the process of capturing the data. We then introduce two tests we used to evaluate the dataset: a) using data science techniques and b) a practice-based application in an AI-robot digital score. The results from these tests are conflicting: from a data science perspective the dataset could be considered questionable, but when applied to a real-world musicking situation performers reported it was transformative and felt to be ‘co-creative. We discuss this duality and pose some important questions for future study. However, we feel that the datatset contains a set of relationships that are useful to explore in the creation of music.

Conference Name 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) 2024
Conference Location Aberystwyth University, Wales
Start Date Apr 3, 2024
End Date Apr 5, 2024
Acceptance Date Jan 23, 2024
Online Publication Date Apr 29, 2024
Publication Date 2024
Deposit Date Jan 24, 2024
Publicly Available Date Apr 30, 2025
Publisher Springer
Volume 14633 LNCS
Pages 373-388
Book Title Lecture Notes in Computer Science
ISBN 9783031569913; 9783031569920
DOI https://doi.org/10.1007/978-3-031-56992-0_24
Keywords dataset; music performance; embodied AI
Public URL https://nottingham-repository.worktribe.com/output/30137175
Related Public URLs https://www.evostar.org/2024/evomusart/
Additional Information First Online: 29 March 2024; Conference Acronym: EvoMUSART; Conference Name: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar); Conference City: Aberystwyth; Conference Country: United Kingdom; Conference Year: 2024; Conference Start Date: 3 April 2024; Conference End Date: 5 April 2024; Conference Number: 13; Conference ID: evomusart2024; Conference URL: https://www.evostar.org/2024/evomusart/; Type: Double-blind; Conference Management System: Easychair; Number of Submissions Sent for Review: 55; Number of Full Papers Accepted: 17; Number of Short Papers Accepted: 8; Acceptance Rate of Full Papers: 31% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.; Average Number of Reviews per Paper: 3; Average Number of Papers per Reviewer: 3; External Reviewers Involved: No

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This file is under embargo until Apr 30, 2025 due to copyright restrictions.




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