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Stéphane Rivaud

Assistant Researcher - PhD Student

music

We are usually unaware of the enormous computing power needed by our brain when listening to music. When trying to make sense of music, we constantly have to classify, sort, remember, structure, and connect a vast number of musical events. Moreover, these events do not only consist of notes, chords, and rhythms but are also characterized by “colors of sound.” These ever-changing frequencies, resulting in complex soundscapes, are at the heart of our musical experiences. I use computer models to simulate the cognitive processes involved when listening to music, to create better tools for music production and music analysis. Creating compositions, musical arrangements, and unique sounds using machine learning and artificial intelligence will lead to a streamlined music production workflow and to entirely different ways to engage with music as a whole.

2017

Sampling Markov Models under Constraints: Complexity Results for Binary Equalities and Grammar Membership

Topics:
music
Authors
Stéphane Rivaud, François Pachet,

CoRR, abs/1711.10436, August, 2017.

2016

Sampling Markov Models under Binary Equality Constraints is Hard

Topics:
music
Authors
Stéphane Rivaud, François Pachet, Pierre Roy,

Journ{'e}es Francophones sur les R{'e}seaux Bay{'e}siens et les Mod{`e}les Graphiques Probabilistes, Clermont-Ferrand, France, June, 2016.