Title: Embedded Machine Learning in Musical Instrument Design

Chris Kiefer, University of Sussex

Abstract:

The 'Musically Embodied Machine Learning' project is currently running at University of Sussex, exploring the challenges and creative opportunities that arise when the processes of training and inference in machine learning are embedded within hybrid musical instruments. It attempts to resolve dissonant approaches that occur when creative machine learning is divided between musical instruments and data science environments, exploring design strategies for integrating ML processes into physical instruments.  The talk will describe motivations behind the project, demonstrate some prototype instruments, and present results from a recent workshop.

Bio:

Chris Kiefer is a Reader in Musical Instrument Design at the University of Sussex, in the Experimental Music Technologies Lab.  He plays an augmented self-resonating cello as half of improv-duo Feeback Cell, and with the feedback-drone-quartet ‘Brain Dead Ensemble’.  As a live coder and solo artist, he performs under the name ‘Luuma’.  His research explores interaction design, complex systems and machine learning in musical instruments. https://luuma.net/

Video: https://www.youtube.com/watch?v=QW_VJJrdJKw

This activity is organized in the frame of the Cátedra UPF-BMAT en Inteligencia Articial y Música (TSI-100929-2023-1). Project funded by Secretaría de Estado de Digitalización e Inteligencia Artificial, and Unión Europea-Next Generation EU