Adventitious Respiratory Classification Using Attentive Residual Neural Networks

Zijiang Yang, Shuo Liu, Meishu Song, Emilia Parada-Cabaleiro, Björn W. Schuller


Every year, respiratory diseases affect millions of people worldwide, becoming one of the main causes of death in nowadays society. Currently, the COVID-19 — known as a novel respiratory illness — has triggered a global health crisis, which has been identified as the greatest challenge of our time since the Second World War. COVID-19 and many other respiratory diseases present often common symptoms, which impairs their early diagnosis; thus, restricting their prevention and treatment. In this regard, in order to encourage a faster and more accurate detection of these kinds of diseases, the automatic identification of respiratory illness through the application of machine learning methods is a very promising area of research aimed to support clinicians. With this in mind, we apply attention-based Convolutional Neural Networks for the recognition of adventitious respiratory cycles on the International Conference on Biomedical Health Informatics 2017 challenge database. Experimental results indicate that the architecture of residual networks with attention mechanism achieves a significant improvement w. r. t. the baseline models.


 DOI: 10.21437/Interspeech.2020-2790

Cite as: Yang, Z., Liu, S., Song, M., Parada-Cabaleiro, E., Schuller, B.W. (2020) Adventitious Respiratory Classification Using Attentive Residual Neural Networks. Proc. Interspeech 2020, 2912-2916, DOI: 10.21437/Interspeech.2020-2790.


@inproceedings{Yang2020,
  author={Zijiang Yang and Shuo Liu and Meishu Song and Emilia Parada-Cabaleiro and Björn W. Schuller},
  title={{Adventitious Respiratory Classification Using Attentive Residual Neural Networks}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={2912--2916},
  doi={10.21437/Interspeech.2020-2790},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2790}
}