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This paper evaluates a wide range of audio-based deep learning frameworksapplied to the breathing, cough, and speech sounds for detecting COVID-19. Ingeneral, the audio recording inputs are transformed into low-level spectrogramfeatures, then they are fed into pre-trained deep learning models to extracthigh-level embedding features. Next, the dimension of these high-levelembedding features are reduced before finetuning using Light Gradient BoostingMachine (LightGBM) as a back-end classification. Our experiments on the SecondDiCOVA 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 thestate-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.

Audio-Based Deep Learning Frameworks for Detecting COVID-19