Music Information Computing
Music Information Computing lies at the intersection of computer and information sciences, mathematics, and music. We develop computational models for musical structures to understand music as a fundamental human trait, and apply these musical structures in novel interaction technologies spanning areas such as music information retrieval; cultural heritage; digital musicology; music recommendation; music and AI; music education; and health, well-being and inclusion.
Our research areas include:
- Computational models for rhythm and meter, melody, harmony, patterns and segments
- Music similarity, classification and retrieval
- Computational annotation tools for studying subjective experiences of music
- Music emotion recognition, musical salience and hooks
- Gamified musical interactions
- Corpus-based computational studies of music
- Artificial intelligence for music analysis and generation
- Fairness and bias mitigation in music recommender systems
- Music technology for health and well-being
- Inclusive music-based games
- Linked data structures for musical heritage
We collaborate across the department involving Artificial Intelligence, Data Science, Human Centered Computing, Software Technology, Multimedia, Social and Affective Computing, and contribute to university-wide topics such as Dynamics of Youth, Digital Humanities, and Game research.