Variational Information Bottleneck for Effective Low-Resource Audio Classification

Shijing Si, Jianzong Wang, Huiming Sun, Jianhan Wu, Chuanyao Zhang, Xiaoyang Qu, Ning Cheng, Lei Chen, Jing Xiao
August 2021
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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
1
Publication
In 22th Annual Conference of the International Speech Communication Association
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Speech
Shijing Si
Shijing Si
Researcher
Jianzong Wang
Jianzong Wang
Honorary Director

[email protected]

Jianhan Wu
Jianhan Wu
Researcher