  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.
