Gabriel Vigliensoni is a Montréal-based musician and researcher. He combines formal musical training and extensive studies in sound recording, music production, music information retrieval, and machine learning to design new approaches to music composition.
In his work, Vigliensoni explores the different stages of the music production workflow, always seeking to transform this process into a playground for learning and experimentation. Throughout his career he has experimented with techno and breakbeat, overlapped krautrock and electronica, explored vocal-driven songs that eschew the standard pop format, used procedural composition techniques, and relied on extended structures, slowing down beats and doing live drum programming to bring liveness and immediacy to digital music production. For Vigliensoni, music is a shared and living experience that is completed when in communication with the audience, which embodies and appropriates it.
Currently a postdoctoral research fellow in the Department of Computing at Goldsmiths, University of London, Vigliensoni is doing practice-based research on the creative capabilities and affordances of the deep learning paradigm for assisting musical composition. He holds a PhD in Music from McGill University.