Hugo Scurto

PhD, postdoctorant researcher

Hugo Scurto (1993, Marseille, Fr) is a researcher, musician and designer. His research employs art, design, and science to craft, prototype, and diffract machine learning in an ecology of music. His practice consists in creating and performing with learning machines that reveal and reshape our musical entanglements with our environments.

Hugo is currently postdoc at EUR ArTeC (Université Paris 8 / EnsadLab), and co-founding member of w.lfg.ng, a music design collective. Before this, he completed a doctoral thesis in Machine Learning and Music Interaction at IRCAM (2016-2019), and was visiting researcher at Department of Computing, Goldsmiths University of London (2015-2016). He graduated in Physics from École Normale Supérieure Paris-Saclay (2011-2016), received the MSc in Engineering from Sorbonne Université (2015), and trained in Popular Music at Cité de la Musique de Marseille (2005-2011).

With the support of a diversity of people, he has published and presented works in international conferences and academic journals such as NIME, ACM TOCHI, DIS, or SIGGRAPH, and contributed to public events in cultural venues such as Ars Electronica, Friche la Belle de Mai, GMEM, or Lutherie Urbaine.

Domaines de recherche (EN) : music, sound art, computational art, machine learning, behavioral objects, new interfaces for musical expression, practice-based research,embodied music cognition, ecoacoustics

Site personnel : http://hugoscurto.com
Twitter : http://twitter.com/hugoscurto
Instagram : https://www.instagram.com/hugoscurto_

Publications (sélection) :
Scurto, H., Caramiaux, B., Bevilacqua, F. (2021). Prototyping Machine Learning Through Diffractive Art Practice. In Proceedings of the 2021 ACM Designing Interactive Systems Conference (DIS’21).

Scurto, H., & Chemla—Romeu-Santos, A. (2021). Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study. In: Kronland-Martinet R., Ystad S., Aramaki M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science, vol 12631. Springer, Cham.

Scurto, H., Van Kerrebroeck, B., Caramiaux, B., Bevilacqua, F. (2021). Designing Deep Reinforcement Learning for Human Parameter Exploration. ACM Transactions on Computer-Human Interaction (TOCHI), 28(1). 1-35.

Scurto, H., Caramiaux, B., Similowski, T., Bianchini, S. (2021). GANspire: Generating Breathing Waveforms for Art-Health Applications. In 5th NeurIPS Workshop on Machine Learning for Creativity and Design.

Audry, S., Dumont-Gagné, R., Scurto, H. (2020). Behaviour Aesthetics of Reinforcement Learning in a Robotic Art Installation. In NeurIPS Workshop on Machine Learning for Creativity and Design 4.0.

Parke-Wolfe, S., Scurto, H., & Fiebrink, R. (2019). Sound Control: Supporting Custom Musical Interface Design for Children with Disabilities. In Proceedings of the 19th International Conference on New Interfaces for Musical Expression (NIME’19).

Lemaitre, G., Scurto, H., Françoise, J., Bevilacqua, F., Houix, O., & Susini, P. (2017). Rising Tones and Rustling Noises: Metaphors in Gestural Depictions of Sounds. PloS one, 12(7), e0181786.

Scurto, H., & Fiebrink, R. (2016). Grab-and-Play Mapping: Creative Machine Learning Approaches for Musical Inclusion and Exploration. In Proceedings of the 42nd International Computer Music Conference (ICMC 2016).