The EIHW-GLAM deep attentive multi-model fusion system for cough-based COVID-19 recognition in the DiCOVA 2021 challenge
- Aiming to automatically detect COVID-19 from cough sounds, we propose a deep attentive multi-model fusion system evaluated on the Track-1 dataset of the DiCOVA 2021 challenge. Three kinds of representations are extracted, including hand-crafted features, image-from-audio-based deep representations, and audio-based deep representations. Afterwards, the best models on the three types of features are fused at both the feature level and the decision level. The experimental results demonstrate that the proposed attention-based fusion at the feature level achieves the best performance (AUC: 77.96%) on the test set, resulting in an 8.05% improvement over the official baseline.
| Author: | Zhao RenORCiD, Yi Chang, Björn W. SchullerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-915722 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/91572 |
| Parent Title (English): | arXiv |
| Type: | Preprint |
| Language: | English |
| Date of Publication (online): | 2021/12/23 |
| Year of first Publication: | 2021 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2022/01/28 |
| First Page: | arXiv:2108.03041 |
| DOI: | https://doi.org/10.48550/arXiv.2108.03041 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
