Total: 1
A fast, efficient and accurate detection method of COVID-19 remains a critical challenge. Many cough-based COVID-19 detection researches have shown competitive results through artificial intelligence. However, the lack of analysis on vocalization characteristics of cough sounds limits the further improvement of detection performance. In this paper, we propose two novel acoustic features of cough sounds and a convolutional neural network structure for COVID-19 detection. First, a time-frequency differential feature is proposed to characterize dynamic information of cough sounds in time and frequency domain. Then, an energy ratio feature is proposed to calculate the energy difference caused by the phonation characteristics in different cough phases. Finally, a convolutional neural network with two parallel branches which is pre-trained on a large amount of unlabeled cough data is proposed for classification. Experiment results show that our proposed method achieves state-of-the-art performance on Coswara dataset for COVID-19 detection. The results on an external clinical dataset Virufy also show the better generalization ability of our proposed method.