Hugo SCURTO ' thesis defence
"Designing With Machine Learning for Interactive Music Dispositifs"
Sergi Jordà, Rapporteur, Universitat Pompeu Fabra
Wendy Mackay, Rapportrice, Université Paris-Saclay
Catherine Pélachaud, Examiner, Sorbonne Université
Anne Sèdes, Examiner, Université Paris 8
Frédéric Bevilacqua, thesis Director, Ircam
Music is a cultural and creative practice that enables humans to express a variety of feelings and intentions through sound. Machine learning opens many prospects for designing human expression in interactive music systems. Yet, as a Computer Science discipline, machine learning remains mostly studied from an engineering sciences perspective, which often exclude humans and musical interaction from the loop of the created systems.
In this dissertation, I argue in favour of designing with machine learning for interactive music systems. I claim that machine learning must be first and foremost situated in human contexts to be researched and applied to the design of interactive music systems. I present four interdisciplinary studies that support this claim, using human-centred methods and model prototypes to design and apply machine learning to four situated musical tasks: motion-sound mapping, sonic exploration, synthesis exploration, and collective musical interaction.
Through these studies, I show that model prototyping helps envision designs of machine learning with human users before engaging in model engineering. I also show that the final human-centred machine learning systems not only helps humans create static musical artifacts, but supports dynamic processes of expression between humans and machines. I call co-expression these processes of musical interaction between humans—who may have an expressive and creative impetus regardless of their expertise—and machines—whose learning abilities may be perceived as expressive by humans.
In addition to these studies, I present five applications of the created model prototypes to the design of interactive music systems, which I publicly demonstrated in workshops, exhibitions, installations, and performances. Using a reflexive approach, I argue that the musical contributions enabled by such design practice with machine learning may ultimately complement the scientific contributions of human-centred machine learning. I claim that music research can thus be led through dispositif design, that is, through the technical realization of aesthetically-functioning artifacts that challenge cultural norms on computer science and music.