Gabriel Vigliensoni
(Photo by Gab del Canto)
Dr Gabriel Vigliensoni, PhD
Thesis supervisor Accepting inquiries
- Assistant Professor in Creative Artificial Intelligence, Design and Computation Arts
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Supervised programs: Design (MDes), Individualized Program (MA, MSc), Individualized Program (PhD)
Research areas: sound and music making, machine learning, human-computer interaction, artificial intelligence, embodied musical interaction, music information retrieval, new interfaces for musical expression, sound design, research-creation, performance, embodiment, interactivity, digital audio, music recommendation, creative machine learning, interactive machine learning
Contact information
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Biography
Gabriel Vigliensoni is an electronic music artist, performer, and researcher whose work currently explores the creative affordances of the machine learning paradigm in the context of sound- and music-making. His practice merges formal musical training with extensive studies and experience in sound recording, music production, music information retrieval, human-computer interaction, and machine learning to explore and develop novel approaches to music composition and performance.
Vigliensoni views sound and music as shared experiences that are completed through audience interaction. Over his extensive career, he has experimented with techno and breakbeat, merged krautrock with electronica, explored vocal-driven songs outside traditional pop formats, and utilized procedural composition techniques to challenge the liveness and immediacy of digital music production.
Vigliensoni's creative work and research have been showcased internationally at venues and conferences such as CCA (QC), CMMAS (MX), IRCAM (FR), MUTEK (QC, CL), ICCC (CA, PO), IKLECTIK (UK), ISEA (CA), NMF (UK), NIME (US), ICMC (US), and ISMIR (US, FR, BR, CN, NL). He earned a PhD in Music Technology from McGill University and currently serves as an Assistant Professor in Creative Artificial Intelligence in the Department of Design and Computation Arts at Concordia University.
vigliensoni performing at MUTEK 2020
Myriam Menard
Teaching activities
Winter 2025
Fall 2024
Winter 2024
Fall 2023
Publications
2025
2024
Tecks, A., T. Peschlow, and G. Vigliensoni. 2024. Explainability Paths for Sustained Artistic Practice. In Proceedings of the the Second International Workshop on eXplainable AI for the Arts at the ACM Creativity and Cognition Conference (XAIxArts2024).
Bryan-Kinns, N., C. Ford, S. Zheng, H. Kennedy, A. Chamberlain, M. Lewis, D. Hemment, Z. Li, Q. Wu, L. Xiao, G. Xia, J. Rezwana, M. Clemens, and G. Vigliensoni. Explainable AI for the Arts 2 (XAIxArts2). In Proceedings of the ACM Creativity and Cognition Conference (C&C ’24).
2023
2022
2021
Vigliensoni, G., E. de Luca, and I. Fujinaga. 2021. Chapter 6: Repertoire: Neume Notation. In Music Encoding Initiative Guidelines edited by J. Kepper et al.
2020
Vigliensoni, G., L. McCallum, E. Maestre, and R. Fiebrink. 2020. Generation and visualization of rhythmic latent spaces. In Proceedings of the 2020 Joint Conference on AI Music Creativity. doi: https://doi.org/10.5281/zenodo.4285422.
Vigliensoni, G., L. McCallum, and R. Fiebrink. 2020. Creating latent spaces for modern music genre rhythms using minimal training data. In Proceedings of the International Conference on Computational Creativity (ICCC’20). doi: https://doi.org/10.5281/zenodo.7415792.
2019
Fujinaga, I., and G. Vigliensoni. 2019. The art of teaching computers: The SIMSSA optical music recognition workflow system. In Proceedings of the 27th European Signal Processing Conference.
2018
Vigliensoni, G., J. Calvo-Zaragoza, and I. Fujinaga. 2018. Developing an environment for teaching computers to read music. In Proceedings
2017
Vigliensoni, G. 2017. Evaluating the performance improvement of a music recommendation model by using user-centric features. PhD dissertation. McGill University.
Vigliensoni, G., D. Romblom, M. P. Verge, and C. Guastavino. 2017. Perceptual evaluation of a virtual acoustic room model. The Journal of the Acoustical Society of America 142(4).
Seventh International Conference on Image Processing Theory, Tools, and Applications.
2016
Vigliensoni, G. and I. Fujinaga. 2016. Automatic music recommendation systems: Do demographic, profiling, and contextual features improve their performance?. In Proceedings of the 17th International Society for Music Information Retrieval Conference. doi: https://doi.org/10.5281/zenodo.1417073.
2015
Fujinaga, I., G. Vigliensoni, and H. Knox. 2015. The making of a computerized harpsichord for analysis and training. International Symposium on Performance Science.
2014
Vigliensoni, G., and I. Fujinaga. 2014. Time-shift normalization and listener profiling in a large dataset of music listening histories. Fourth annual seminar on cognitively based music informatics research.
2013
Vigliensoni, G., J. A. Burgoyne, and I. Fujinaga. 2013. Musicbrainz for the world: the Chilean experience. In Proceedings of the International Society for Music Information Retrieval Conference.
2012
Vigliensoni, G., and M. Wanderley. 2012. A quantitative comparison of position trackers for the development of a touch-less musical interface. In Proceedings of the New Interfaces for Musical Expression Conference.
2011
2010
Vigliensoni, G., and M. Wanderley. 2010. Soundcatcher: Explorations in audio-looping and time-freezing using an open-air gestural controller. In Proceedings of the International Computer Music Conference.
Awards and Funding
2023–2025. PI, Explore and Create | Research Creation. Canada Council for the Arts
2023–2025. PI, Faculty Research Development Program. Concordia University
2022–2023. PI, Knowledge Mobilization Grant. Research Creation. Fonds de Recherche du Québec—Société et Culture (FRQSC)
2020–2022. PI, Postdoctoral Research Creation. Fonds de Recherche du Québec—Société et Culture (FRQSC)
Artistic performances
Music Releases
2022 vigliensoni CMD – Scintillation (Dance Across Borders DAB003)