I am a doctor of computer science interested in understanding, analyzing and modeling the creative processes used by musicians in their compositions, improvisations and stage performances, as well as creating new computer tools for musician-machine interactions. I use formal and empirical methods from theoretical computer science, natural language processing and machine learning. During my research, I like to perform both quantitative and qualitative studies. I frequently gather feedback during discussions with professional musicians and musicologists using ethnography and grounded theory methodology.
I am also a trained musician with a Music Theory diploma with special interests for jazz, rock/zeuhl, avant-garde, electronic music and vaporwave.
⭔ Research interests:
- Computer Music
Music generation, human-machine interaction, computational musicology, music structure analysis, machine learning applied to music, music cognition.
- Theoretical Computer Science
Formal language theory, automata and semigroups theory, text algorithmics.
- Other Mathematics Skills
Signal processing for sound and music, probabilistic graphical models, algebra, combinatorics.
Scientific Reports, 11 15990 (2021) :
New paper: S. A. Herff, L. Taruffi, G. Cecchetti, K. Déguernel. Music influences vividness and content of imagined journeys in a directed visual imagery task. Scientific Reports, 2021.
AI Song Contest 2021:
Gulluni x Algomus: 3rd place (out of 38) for the song « The Last Moment Before You Fly ».
Sound and Music Computing 2021:
New paper: K. Déguernel, A. Parmentier, C. Frei. A modular tool for automatic Soundpainting Query Recognition and music generation in Max/MSP. Proceedings of the Sound and Music Computing Conference, 2021.
Supplementary material for SPQR
Swiss National Science Foundation Spark grant accepted!
“Wanderful music: a systematic investigation into music-induced mind wandering.”, co-investigator with Dr. S. Herff and G. Cecchetti.
The project starts January 2021.