dScience Lunch Seminar: Computational music analysis: Significance, challenges, and our proposed approach

Music is something that we mostly all appreciate, yet it remains a hidden and enigmatic concept for many of us. Music notation, in the form of music scores, facilitates practicing and enhances the understanding of the richness of musical works. However, acquiring musical scores for any music performance is a tedious and demanding task (called music transcription) that demands considerable proficiency. Hence the interest of computational automation. But music is not just notes, it is also melody, rhythm, themes, timbre, and very subtle aspects such as form. While many of us may not be consciously familiar with these concepts, they still have a subconscious influence on our aesthetic experience. Interestingly, it often happens that the more we consciously understand the underlying language of music, the more we tend to appreciate and enjoy it. Therefore, there is value in creating computational tools that can automate and enhance these types of analyses.

 

The presenters' past work resulted in the creation of Matlab's MIRtoolbox, which measures a broad range of musical characteristics directly from audio through signal processing techniques. Currently, the MIRAGE project prioritises music transcription (with a particular focus on Norwegian folk music), blending neural-network-based deep learning with conventional rule-based models. Through this project, they highlight the importance of acknowledging the interconnectedness between all musical elements. Additionally, they have crafted animated visualisations to make analyses more accessible to the general public and are aiming to make music transcription technology available to the public, with support from UiO Growth House.

Olivier Lartillot is a researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, at the University of Oslo. He obtained a funding from the Research Council of Norway under the FRIPRO-IKTPLUSS program, for a project called MIRAGE - A Comprehensive AI-Based System for Advanced Music Analysis (2020-2023). He obtained his PhD at the Ircam music research centre in Paris and was, among other, Academy of Finland research fellow at the University of Jyv?skyl?.

 

Lars L?berg Monstad is a research assistant in the MIRAGE project. His Master thesis at the Department of Music of the University of Oslo focused on using Artificial Intelligence to create Electronic Dance Music. He collaborates with music producers in the task of automated music transcription.