(Funding Eligibility: UK applicants only)
The aim of this PhD project is to investigate information theoretic methods for analysis of sounds. The Information Bottleneck (IB) method has emerged as an interesting approach to investigate learning in deep learning networks and autoencoders. This project will investigate information-theoretic approaches to analyse sound sequences, both for supervised learning methods such convolutive and recurrent networks, and unsupervised methods such as variational autoencoders. The project will also investigate direct information loss estimators, and new information-theoretic processing structures for sound processing, for example involving both feed-forward and feedback connections inspired by transfer information in biological neural networks.
We particularly encourage applications from candidates with disabilities, Black, Asian and Minority Ethnic candidates and female candidates as these groups are underrepresented throughout our area. We also welcome enquiries from self-funded and part-funded candidates.
For informal enquiries on opportunities related to AI for Sound, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk).
More information and how to apply: https://www.surrey.ac.uk/fees-