Information theoretic learning for sound analysis

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 as 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.