Music Informatics

The Music Informatics research group is a specialized research team within the Department of Computing.

Music Informatics includes the study of computational models of music analysis, music generation, and music performance.

Interests of the Music Informatics group include statistical modelling, computational musicology, music knowledge representation, pattern discovery, and music e-learning. The group is also interested in wider aspects of modelling sequential structures, such as financial time series, biological sequences, and text, and the novel application of techniques from these areas to music.

Main Research Activities

• Music data mining. Statistical models of music score and MIDI data, with the goal of genre classification, music generation, and style emulation.
• Computational musicology. Algorithms for music segmentation, representation, hierarchical structuring, and analysis. Algorithms for representation and discovery of recurrent patterns in music score data.
• Music knowledge representation. Representation of music on multiple viewpoints and levels, functional programming and music, mathematical structures for music, standardisation activities in the MPEG ad hoc group on Symbolic Music Representation.
• Music e-learning. Exploitation of new pedagogical paradigms (e.g., self learning, distance learning, co-operative work) for teaching music.

Group members

Current

Publications