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Aiming to automatically detect COVID-19 from cough sounds, we propose a deepattentive multi-model fusion system evaluated on the Track-1 dataset of theDiCOVA 2021 challenge. Three kinds of representations are extracted, includinghand-crafted features, image-from-audio-based deep representations, andaudio-based deep representations. Afterwards, the best models on the threetypes of features are fused at both the feature level and the decision level.The experimental results demonstrate that the proposed attention-based fusionat the feature level achieves the best performance (AUC: 77.96%) on the testset, resulting in an 8.05% improvement over the official baseline.

The EIHW-GLAM Deep Attentive Multi-model Fusion System for Cough-based COVID-19 Recognition in the DiCOVA 2021 Challenge