  This paper evaluates a wide range of audio-based deep learning frameworks
applied to the breathing, cough, and speech sounds for detecting COVID-19. In
general, the audio recording inputs are transformed into low-level spectrogram
features, then they are fed into pre-trained deep learning models to extract
high-level embedding features. Next, the dimension of these high-level
embedding features are reduced before finetuning using Light Gradient Boosting
Machine (LightGBM) as a back-end classification. Our experiments on the Second
DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score,
sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%,
respectively. Based on these scores, our method outperforms the
state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00%
and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.
