VGGish-BiLSTM-Attention for COVID-19 Identification Using Cough Sound Analysis

Respiratory diseases remain one of the major problems of public health, and early identification of these diseases benefits patient management, disease treatment and contagion control. During COVID-19 pandemic, using cough sound analysis to classify respiratory diseases seems promising. In this study, a deep learning network (VGGish-BiLSTM-attention) is implemented. It employs pre-trained VGGish structure, bidirectional long-short-term-memory (BiLSTM) and attention module, and besides, an output layer is followed for feature fusion and disease classification. Meanwhile, data augmentation, cough sound representation and transfer learning are used for performance boosting. On the COUGHVID database, a binary classification problem (“maybe-covid” versus “no-covid”) is formed, and the proposed network achieves the state-of-the-art result (accuracy 92.41%; precision, 92.59%; recall, 91.97%; F1-score, 92.23%). The ablation study indicates that data augmentation contributes the most with more than 12% increase, and VGGish, BiLSTM and attention module are also important in cough sound based disease analysis. In the future work, more efforts could be made to finely stratify cough sounds and to design advanced models for accurate disease classification and personalized medicine.