The Music Informatics research group is a specialized research team within the Department of Computer Science.
Music Informatics includes the study of computational models of music analysis, music generation, and music performance. Interests of the Music Informatics group include machine learning, computational musicology, audio signal processing, music knowledge representation, and applications. The group is also interested in wider aspects of machine learning on signals and symbolic (discrete) data, such as bioacoustics and industrial signal and knowledge analysis, and the novel application of techniques from these areas to music.
- Machine learning for music analysis classification, music generation and style emulation.
- Adaptive models of musical scores, MIDI, and audio data,
- Music signal analysis: automatic music transcription, multi-pitch detection, onset detection, instrument identification.
- Audio signal processing for classification, similarity estimation.
- Algorithms for music segmentation, voice segregation, hierarchical structuring, and analysis.
- Computational musicology.
- Music knowledge representation, standardisation activities in the MPEG ad hoc group on Symbolic Music Representation.
- Music applications, such as e-learning.
- Scientific and industrial applications of signal processing and machine learning.