Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition

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  • University of Augsburg
  • Imperial College London
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Original languageEnglish
Article number29
JournalActa Acustica
Volume6
Issue number29
Publication statusPublished - 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

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Cite this

Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition. / Ren, Zhao; Chang, Yi; Nejdl, Wolfgang et al.
In: Acta Acustica, Vol. 6, No. 29, 29, 25.07.2022.

Research output: Contribution to journalArticleResearchpeer review

Ren Z, Chang Y, Nejdl W, Schuller BW. Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition. Acta Acustica. 2022 Jul 25;6(29):29. doi: 10.1051/aacus/2022029, 10.15488/12807
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title = "Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition",
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.",
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note = "Funding Information: This work was partially supported by the BMBF project LeibnizKILabor with grant No. 01DD20003, and the Horizon H2020 Marie Sk{\~o}{\~o}lodowska-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. ",
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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.

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