Algomus – Algorithmic Musicology – is a research team in Digital Humanities, in Musical Information Retrieval (MIR). The team focuses on high-level modeling, analysis, and co-creative music generation, catering to music theorists, musicians, music enthusiasts, and a wider audience. In collaboration with music theorists, Algomus primarily investigates symbolic data, which includes music scores, tablatures, chord grids, and other forms of music data and metadata. The team models patterns, melodies, harmony (including chords, chord progressions, and cadences), rhythms, texture, and ultimately, the structure of music.

Algomus integrates musicological expertise with computer science methods, encompassing text algorithmics, data mining, machine learning (ML), and artificial intelligence (AI). The team is particularly interested in designing explainable algorithmic and ML/AI approaches, aiming to elucidate the essence of music and provide individuals with tools to understand, engage with, and ultimately enjoy music,along two key axes:

• Music modeling and analysis for systematic/empirical musicology (Computational Music Analysis), offering fresh insights into musical forms, genres, styles, and cultural contexts.

• Co-creative music modeling and generation, exploring aspects and dynamics of musical co-creativity, with a particular emphasis on high-level structures within symbolic data.

Algomus collaborates with musicians, music educators, artists, and music companies. The web platform Dezrann, designed to share music analysis, finds application in both MIR research and music education, aiding people in thinking about and discussing music. The team contributes to science&arts and music&health projects, popular science events, and openly publishes methods, code, and datasets.