# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
## 1 Introduction
Additionally, they used the LightGBM model for classification, and performed model tuning through the method of k-fold cross-validation. Finally, the area under the ROC curve is 0.96. [PERSON] et al. [24] extracted features such as Mel Frequency Cepstral Coefficients (MFCC) and zero-crossing rates from the audio data of the coswara public dataset and used them for model training. In their experiment, a total of seven machine learning classifiers were trained and evaluated, including LR (logistic regression) model, support vector machine model, k-nearest neighbor algorithm, multilayer perceptron (MLP), long short-term memory (LSTM), CNN, and 50-layer depth residual neural network (resnet50). Among them, the performance of LSTM, CNN, and resnet50 classifiers is better than other architectures. In terms of COVID-19 detection, many research teams in China have also made many contributions. Researchers from the Institute of Artificial Intelligence of Beijing University of Posts and Telecommunications expanded the corpus of cough sounds and speech clips. They extracted VQ features from the audio and used Vlad encoding to make the features more representative and improve the performance of the algorithm [25]. The research of the team formed by the University of Science and Technology of China and iFLYTEK won two championships in the second DiCOVA COVID-19 Sound Signal Detection Challenge held by ICASSP2022 [26]. Their design idea is to use both supervised and self-supervised pre-training schemes, and finally fuse the prediction results, which provides an outstanding AUC index value with 0.88. Considering that one type of the acoustic features is relatively single or the acoustic features do not have strong representation abilities, we adopted a combination of three features, which fully considered the characteristics of human hearing; especially the pre-training model Vggish is trained on the large-scale audio dataset, and has strong domain adaptability. Three features combination makes our proposed method have a strong generalization ability, and can be better applied to the situation where the training data are limited. Different from a single neural network structure, our designed multi-headed convolutional neural networks (MHCNNs) structure can fuse different abstract representations from each head and combine them to achieve a more comprehensive data representation. At the same time, from the perspective of information fusion, our designed structure of each head of a MHCNN is different. By integrating information from multiple heads, the network can benefit from different perspectives, resulting in richer representation and a better decision-making ability. This paper will adopt the deep learning method of MHCNNs to introduce the intelligent diagnosis of audio recognition technology into the medical diagnosis process, and our experimental results have good performance. This will be a low-risk and high-efficiency large-scale epidemic surveillance method. At the same time, this method will effectively reduce medical expenses in many countries, thereby indirectly improving the quality of national healthcare services [27; 28].