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

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.

Citation

Vear, C., Poltronieri, F., DiDonato, B., Zhang, Y., Benerradi, J., Hutchinson, S., Turowski, P., Shell, J., & Malekmohamadi, H. (2024, April). Building an Embodied Musicking Dataset for co-creative music-making. Presented at 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) 2024, Aberystwyth University, Wales

• Presentation Conference Type: Edited Proceedings
• Conference Name: 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) 2024
• 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
• Peer Reviewed: Peer Reviewed
• Volume: 14633 LNCS
• Pages: 373-388
• Series Title: Lecture Notes in Computer Science (LNCS)
• Series ISSN: 1611-3349
• Book Title: Lecture Notes in Computer Science
• ISBN: 9783031569913
• 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