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 and sound analysis and generation, and music performance. Interests of the Music Informatics Research Group include music information retrieval and computational musicology, music signal analysis, music knowledge representation, and music applications, such as e-learning and games. The group is also interested in wider aspects of machine learning, sequential structure modelling and audio signal processing, and novel cross-domain application of techniques in these areas. For an overview presentation showcasing MIRG activities, click here.
Main Research Activities
- Music information retrieval. Adaptive models of score, MIDI, and audio data, with the goal of genre classification, similarity, style and user modelling.
- Music signal analysis. Automatic music transcription, multi-pitch detection, onset detection, instrument identification.
- Computational musicology. Algorithms for music segmentation, representation, hierarchical structuring, and analysis.
- Music knowledge representation. Representation of music on multiple levels, logical structures for music, standardisation activities.
- Applications: exploration and exploitation of new technological approaches for applications such as music search and recommendation, as well as analysis and classification of biological and industrial sounds.
We are collecting a new music annotation dataset including similarity, tempo and rhythm data: Our multi-player music game “Spot the Odd Song Out” can be played here.