Short Communication

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

¹ Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, 86159 Augsburg, Germany
² L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany
³ GLAM – Group on Language, Audio & Music, Imperial College London, SW7 2AZ London, UK

^* Corresponding author: zren@l3s.de

Received: 6 August 2021
Accepted: 5 July 2022

Abstract

Coughs sounds have shown promising as a 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.