Variational Information Bottleneck for Effective Low-Resource Audio Classification

Schematic diagram of CNN+VIB framework

Abstract

Large-scale deep neural networks (DNNs) such as convolutional neural networks (CNNs) have achieved impressive performance in audio classification for their powerful capacity and strong generalization ability. However, when training a DNN model on low-resource tasks, it is usually prone to overfitting the small data and learning too much redundant information. To address this issue, we propose to use variational information bottleneck (VIB) to mitigate overfitting and suppress irrelevant information. In this work, we conduct experiments on a 4-layer CNN. However, the VIB framework is ready-to-use and could be easily utilized with many other state-of-the-art network architectures. Evaluation on a few audio datasets shows that our approach significantly outperforms baseline methods, yielding ≥ 5.0% improvement in terms of classification accuracy in some low-source settings.

Type
Publication
In 22th Annual Conference of the International Speech Communication Association
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Shijing Si
Shijing Si
Researcher
Jianhan Wu
Jianhan Wu
Researcher
Ning Cheng
Ning Cheng
Director