ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

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

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.


doi: 10.21437/Interspeech.2021-2028

Cite as: Si, S., Wang, J., Sun, H., Wu, J., Zhang, C., Qu, X., Cheng, N., Chen, L., Xiao, J. (2021) Variational Information Bottleneck for Effective Low-Resource Audio Classification. Proc. Interspeech 2021, 591-595, doi: 10.21437/Interspeech.2021-2028

@inproceedings{si21_interspeech,
  author={Shijing Si and Jianzong Wang and Huiming Sun and Jianhan Wu and Chuanyao Zhang and Xiaoyang Qu and Ning Cheng and Lei Chen and Jing Xiao},
  title={{Variational Information Bottleneck for Effective Low-Resource Audio Classification}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={591--595},
  doi={10.21437/Interspeech.2021-2028}
}