Audio/acoustics machine learning for intelligent sound reproduction

The University of Surrey is offering a fully funded PhD studentship on the topic of Audio/acoustics machine learning for intelligent sound reproduction, with industrial partner Bang & Olufsen. Nowadays it is commonplace for AI systems to recommend “what” media content users may choose to consume, yet typical audio reproduction systems cannot readily adapt “how” they present the experience. This technical project designs the affordances of an advanced spatial audio system with awareness of the user's intentions, choices and environment in order to adapt the sound reproduction intelligently. The engineering challenges are to provide users with the controls they would like to have in order to adjust the behaviour of their audio system, for example, boosting the bass response interacting with the room to provide greater envelopment or utilising the spatial extent of a highly-capable immersive reproduction system to render the content more intelligibly.

Successful candidates will be supervised by Professor Philip Jackson and Professor Wenwu Wang in the Centre for Vision, Speech and Signal Processing (CVSSP) and Surrey Institute for People Centred Artificial Intelligence, at the University of Surrey.  The PhD student will be based at the CVSSP. They will benefit from resources from CVSSP, the Surrey Institute for People-Centred AI, and potential secondment opportunities at Bang & Olufsen.

Open to any UK or international candidates.

You will need to meet the minimum entry requirements for our Vision, Speech and Signal Processing PhD PhD programme.

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with Distinction (or 70% average) and a strong interest in pursuing research in this field. Additional experience which is relevant to the area of research is also advantageous.  

English language requirements: 

IELTS Academic 6.5 or above (or equivalent) with 6.0 in each individual category.