PhD Candidate in Music Technology
I am a Ph.D. student working under Ichiro Fujinaga in the Music Technology Area of the Schulich School of Music, McGill University. My research interests center around analysis of repeated material in symbolic music; in particular, how machine-learning models can learn from long-term dependencies in music (e.g., verse-chorus forms, sonata forms) to make predictions and correct errors in scores. I also research musical pattern discovery algorithms, and how existing examples of annotated patterns can be used as examples to filter the large number of patterns that these algorithms often retrieve.
Before my arrival at McGill, I was a research assistant in the Computational Epidemiology Research Laboratory at the University of North Texas, where I investigated models of traffic flow in disaster response scenarios.
I’m a guitar player, and perform live under the band name “Water Gun Water Gun Sky Attack” every so often.
de Reuse, Timothy. 2019. “A Machine Learning Approach to Pattern Discovery in Symbolic Music.” Master’s Thesis, Montreal, Canada: McGill University.
de Reuse, Timothy, and Ichiro Fujinaga. 2019. “Pattern Clustering in Monophonic Music by Learning a Non-Linear Embedding from Human Annotations.” In Proceedings of the 20th International Society for Music Information Retrieval Conference. Delft, Netherlands.
de Reuse, Timothy, and Ichiro Fujinaga. 2019. “Robust Transcript Alignment on Medieval Chant Manuscripts.” In Proceedings of the 2nd International Workshop on Reading Music Systems. Delft, Netherlands.