Information theoretic learning for sound analysis

本博士计画的目的是研究声音分析的资讯理论方法。信息瓶颈（IB）方法已成为研究深度学习网络和自动编码器学习的一种有趣方法。该项目将研究信息论方法来分析声音序列，包括监督学习方法，如卷积和循环网络，以及无监督方法，如变分自编码器。该项目还将研究直接信息损失估计器，以及用于声音处理的新的信息理论处理结构，例如，涉及受生物神经网络中传递信息启发的前馈和反馈连接。

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.