Learning the Structure of Music

This project is aimed at the development of models and tools for the application of novel probabilistic machine learning techniques to the analysis of music. The underlying theme of the project is the learning of patterns linking different data arising simultaneously from the same piece of music. The sources of data will be as follows: a) musical scores (MIDI format), b) audio (recordings of the pieces), c) worm data (charting performance information), d) EEG data (of subjects listening to the music) and d) fMRI data (of subjects listening to the music).The linking patterns that we will be seeking involve pairs of data streams as follows: a) musical scores with worm data, b) musical scores with fMRI data and c) audio with EEG data. The first pair will be used to identify typical performance patterns of particular performers. The second and the third pairs will be used to identify the effects in the brain of particular musical patterns (such as melodic sequences, musical phrasings, harmonic progressions, etc.).The project will advance our understanding of the relationship between musical structure and performance and experience. The potential of such developments is quite wide ranging, with potential application in music therapy and entertainment. For example, it will contribute to the development of systems for artificial performance of music imitating the style of a performer on pieces that he or she may have never played before and systems for musical composition tailored to achieve specific effects (or moods) on the listener.