Music Interestingness in the Brain
Measuring interestingness of a song when one is listening to the song will not only shed some light on individual music perception, allowing personalized music recommendation, but also open possibility of using music songs as a brain stimulus. This project aims to automatically measure interestingness of a music songs in the brain using Ear-EEG.
An Ear-EEG device will be used to measure the brain signal (EEG) in the ear canals when one is listening to a song, which is then assessed by machine learning algorithms (potentially deep neural networks) to map the recorded EEG signal into an interestingness measure. Data collection will be carried out and a cohort of young and healthy subjects will be recruited for this purpose. This data will allow exploring different machine learning algorithms and techniques for interestingness modelling. Personalisation and multi-modal modelling, that combines music information (either raw signals or high-level musical features, e.g. melody, music genre, etc.) and the EEG, will also be investigated. This is a joint project with the Centre for Ear-EEG, Aarhus University, and the candidate is expected to work with academics in both C4DM and the Centre for Ear-EEG.
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