@inproceedings{abafa59cbd7e4f4b8b91646e90032eb3,
title = "A GLANCE-AND-GAZE NETWORK FOR RESPIRATORY SOUND CLASSIFICATION",
abstract = "A plethora of great successes has been achieved by the existing convolutional neural networks (CNN) for respiratory sound classification. Nevertheless, simultaneously capturing both the local and global features can never be an easy task due to the limitation of a CNN's structure. In this contribution, we propose a novel glance-and-gaze network to address the aforementioned issue. The glance block aims to learn global information, while the gaze block is responsible for learning local patterns and suppressing the noises that attenuates the final performance. In the proposed method, both the global and local information can be extracted. Moreover, the spectral and temporal representations can be learnt via a feature fusion module. Experimental results on the largest public respiratory sound database demonstrate that the proposed model outperforms the state-of-the-art methods.",
keywords = "Computer Audition, Digital Health, Feature Fusion, Glance-and-Gaze Network, Respiratory Sound Classification",
author = "Shuai Yu and Yiwei Ding and Kun Qian and Bin Hu and Wei Li and Schuller, {Bj{\"o}rn W.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICASSP43922.2022.9746053",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "9007--9011",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
address = "United States",
}
