Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

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

Hugo Scurto
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Résumé

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 instrument focused on embodied musical interaction. It then describes how this instrument 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 build on this to expose our personal insights and reflections on the multidisciplinary opportunities framed by machine learning for computer music.
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Dates et versions

hal-02408699 , version 1 (13-12-2019)
hal-02408699 , version 2 (17-03-2021)

Identifiants

  • HAL Id : hal-02408699 , version 1

Citer

Hugo Scurto, Axel Chemla--Romeu-Santos. Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study. 14th International Symposium on Computer Music Multidisciplinary Research (CMMR’19), Oct 2019, Marseille, France. ⟨hal-02408699v1⟩
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