# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 3 Proposed Method
### Model  
In this study, a model combining ResNet-50 and a DNN proposed by [PERSON] et al. [22] was used. ResNet-50 is a convolutional neural network composed of 50 layers that allows for stable learning while the depth of the model increases. The DNN is an artificial neural network (ANN) that consists of several hidden layers between the input layer and the output layer. The mel spectrogram image obtained from the cough segment was input to ResNet-50, the feature set was input to the DNN, and the inputs were trained.  
In the first branch, ResNet-50 was trained with the mel spectrogram image of (224, 224, 3) as the input. In ResNet-50, an image of size (224, 224, 3) went through multiple  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Bhattacharyya Distance** \\ \hline MFCC & 0.207171 \\ \(\Delta^{2}\)-MFCC & 0.149195 \\ \(\Delta\)-MFCC & 0.099828 \\ Spectral Contrast & 0.090616 \\ Chroma & 0.063358 \\ Spectral Flatness & 0.057523 \\ Spectral Bandwidth & 0.046912 \\ Spectral Roll-Off & 0.032971 \\ RMS Energy & 0.018301 \\ Spectral Centroid & 0.016368 \\ Zero-Crossing Rate & 0.002629 \\ Onset & 0.002387 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bhattacharyya distance of each feature.  
convolutional, activation, and pooling layers to reduce the size of the image while extracting features. This process was repeated multiple times, with each iteration reducing the size of the feature map while increasing the number of filters used by the network. The output of the final pooling layer was a tensor with size (7, 7, 2048), which represented a compact representation of the input image. The output went through global average Pooling and global max pooling separately. The global pooling layer is a method of replacing values of the same channel with one average or maximum value. Overfitting can be prevented because the parameters are reduced. If an input of size (height, width, channel) passes through the global pooling layer, it becomes (1, 1, channel). The two outputs obtained through this process were connected after batch normalization and dropout were performed. In batch normalization, the activations of each neuron in a layer are normalized using the mean and standard deviation of the activations in a subset of the training data. The normalized activations are then scaled and shifted using learned parameters. In dropout, neurons are randomly dropped out during each iteration of training, with a specified probability. These processes help to ensure that the network will be able to generalize new data well, while still being able to learn effectively from the training data. The second branch took a 46-dimensional feature set as input. The feature set was input to a dense layer consisting of 256 nodes, and batch normalization and dropout were performed. The output became the input to a dense layer with the same number of nodes, and batch normalization and dropout were performed once more. The above process was performed in the same way for the dense layer consisting of 64 nodes to obtain another output. The two outputs were then connected. The outputs obtained through the first branch and the second branch were connected and became the input to the dense layer, and batch normalization and dropout were performed. Finally, the sigmoid function was used to calculate the value, to distinguish whether the input was the cough sound of COVID-19-positive individual or negative individual. Figure 2 shows the flow chart of the model.  
Figure 2: Flow chart of the model.