Algomus team

Algorithmic Musicology

Leader: Mathieu Giraud



Sheet music describes notes, harmonies and rhythms. For centuries, music notation has been the primary channel to communicate, exchange and preserve works of Western music. Today, digital humanities link computational methods to cultural heritage and humanities research. How can computers help to model digital scores and ultimately to understand music ?

The Algomus emergent team is a collaboration between the CRIStAL lab (UMR 9189) and the MIS lab (Université de Picardie Jules Verne, Amiens). Algomus does research in computational music analysis. The team proposes algorithms to analyze music scores, combining musicological knowledge and computer science methods in text algorithmics, data mining and machine learning. The team studies music patterns, chords and chord progressions, music texture and other music notions and ultimately aims to study the high-level structure of music such as musical forms. Algomus also works on modeling annotated scores for musicians, music learners, music lovers and everyone else. Algomus collaborates with music theorists, music teachers and artists, and contributes to science and arts projects and popular science events.




Vanessa nina Borsan

Indexation de motifs mélodiques et harmoniques

Louis Couturier

Modélisation de la texture musicale pour l'analyse et l'aide à la composition

Alexandre D'hooge

Elaboration d’outils d’intelligence artificielle pour assister la composition de tablatures pour guitare

Dinh-viet-toan Le

Approches de traitement automatique du langage naturel dans le domaine musical : adaptabilité, performance et limites

Francesco Maccarini

Modélisation informatique de l'écriture symphonique