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Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study

Hugo Scurto 1 Axel Chemla--Romeu-Santos 2, 3
1 Interaction Son Musique Mouvement [Paris]
STMS - Sciences et Technologies de la Musique et du Son : UMR 9912
2 Repmus - Représentations musicales
STMS - Sciences et Technologies de la Musique et du Son
Abstract : This paper presents a multidisciplinary case study of practice with machine learning for computer music. It builds on the scientific study of two machine learning models respectively developed for data-driven sound synthesis and interactive exploration. It details how the learning capabilities of the two models were leveraged to design and implement a musical interface focused on embodied musical interaction. It then describes how this interface was employed and applied to the composition and performance of aego, an improvisational piece with interactive sound and image for one performer. We discuss the outputs of our research and creation process, and expose our personal reflections and insights on transdisciplinary research opportunities framed by machine learning for computer music.
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Submitted on : Wednesday, March 17, 2021 - 5:18:07 PM
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Hugo Scurto, Axel Chemla--Romeu-Santos. Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study. Richard Kronland-Martinet; Sølvi Ystad; Mitsuko Aramaki. Perception, Representations, Image, Sound, Music. 14th International Symposium, CMMR 2019, Marseille, France, October 14–18, 2019, Revised Selected Papers, 12631, Springer, pp.665-680, 2021, Lecture Notes in Computer Science, 978-3-030-70209-0. ⟨10.1007/978-3-030-70210-6_43⟩. ⟨hal-02408699v2⟩

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