Details
| Original language | English |
|---|---|
| Article number | 29 |
| Journal | Acta Acustica |
| Volume | 6 |
| Issue number | 29 |
| Publication status | Published - 25 Jul 2022 |
Abstract
Coughs sounds have shown promising as-potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline.
Keywords
- Attention mechanism, Complementary representation, Cough sound, COVID-19, Ensemble learning
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Acoustics and Ultrasonics
- Computer Science(all)
- Computer Science Applications
- Health Professions(all)
- Speech and Hearing
- Engineering(all)
- Electrical and Electronic Engineering
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In: Acta Acustica, Vol. 6, No. 29, 29, 25.07.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition
AU - Ren, Zhao
AU - Chang, Yi
AU - Nejdl, Wolfgang
AU - Schuller, Björn W.
N1 - Funding Information: This work was partially supported by the BMBF project LeibnizKILabor with grant No. 01DD20003, and the Horizon H2020 Marie Skõõlodowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project under grant agreement No. 766287 (TAPAS). The authors would thank the friendly discussions from their colleagues Lukas Stappen and Vincent Karas.
PY - 2022/7/25
Y1 - 2022/7/25
N2 - Coughs sounds have shown promising as-potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline.
AB - Coughs sounds have shown promising as-potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline.
KW - Attention mechanism
KW - Complementary representation
KW - Cough sound
KW - COVID-19
KW - Ensemble learning
UR - http://www.scopus.com/inward/record.url?scp=85135094432&partnerID=8YFLogxK
U2 - 10.1051/aacus/2022029
DO - 10.1051/aacus/2022029
M3 - Article
AN - SCOPUS:85135094432
VL - 6
JO - Acta Acustica
JF - Acta Acustica
IS - 29
M1 - 29
ER -