## 3 Experiment Result
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{Normal} & STFT based + our model & 0.98 & 1.0 & 0.98 & 0.99 \\  & MFCC based + our model & 0.98 & 1.0 & 0.98 & 0.99 \\ \hline \multirow{3}{*}{Rubbing} & STFT based + our model & 1.0 & 1.0 & 1.0 & 1.0 \\  & MFCC based + our model & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \multirow{3}{*}{Unbalance} & STFT based + our model & 1.0 & 0.98 & 1.0 & 0.99 \\  & MFCC based + our model & 1.0 & 0.98 & 1.0 & 0.99 \\ \hline \multirow{3}{*}{Misalignment} & STFT based + our model & 1.0 & 1.0 & 1.0 & 1.0 \\ \cline{1-1}  & MFCC based + our model & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental Results. In Figure 8, the left figure compares Validation Loss and Train Loss, and the right figure compares Train Accuracy and Validation Accuracy. Each model is all finished before the epochs set before running out due to Early Stopping. Experiments have confirmed that the model proposed in this paper shows better accuracy than the comparison model in Table 5. Although the VGG19 [6]-based model proposed in this paper ended later than SqueezeNet, it was confirmed that it is superior in terms of loss and acceleration stability. Table 5 is a representation of the results of Figure 8, which is the result of training the proposed deep learning model and the existing deep learning models by adding noise to the data. As shown in Table 5, Transfer Learning was concluded to help produce more robust results. We confirm that the model proposed in this paper has the least learning accuracy and learning loss, and the least validation loss compared to VGG19. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Algorithm** & **Parameters** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} } & MLP [5] & 74,500 & 0.95 & 0.9525 & 0.955 & 0.9525 \\  & GA-SVM [3] & - & 0.51 & 0.507 & 0.505 & 0.5025 \\  & PCA-SVM [3] & - & 0.96 & 0.9625 & 0.9675 & 0.965 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Deep Learning \\ \end{tabular} } & SqueezeNet [8] & **737,476** & **0.995** & 0.982 & 0.985 & 0.985 \\  & Alex Net [7] & 57,020,228 & **0.995** & **0.995** & **0.995** & **0.995** \\  & VGG19 [6] & 139,597,636 & **0.995** & **0.995** & **0.995** & **0.995** \\  & Our & 20,037,444 & **0.995** & **0.995** & **0.995** & **0.995** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Train noise-added data to compare test results with existing deep learning models. Figure 8: Training loss and Validation loss (**Up**) and accuracy (**Down**) rate according to epoch number in the experiment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  &
\begin{tabular}{c} **Transfer** \\ **Learning** \\ \end{tabular} & **Epoch** & **Train Accuracy** & **Train Loss** & **Valid Accuracy** & **Valid Loss** \\ \hline \multirow{3}{*}{Ours} & Yes & 23 & **0.991** & **0.033** & **0.995** & 0.007 \\  & No & 22 & 0.988 & 0.050 & **0.995** & 0.009 \\ \hline \multirow{2}{*}{SqueezeNet [8]} & Yes & 22 & 0.990 & 0.066 & **0.995** & **0.004** \\  & No & 114 & 0.760 & 0.364 & 0.707 & 0.107 \\ \hline \multirow{2}{*}{AlexNet [7]} & Yes & 22 & 0.986 & 0.053 & **0.995** & 0.007 \\  & No & 12 & 0.271 & 1.385 & 0.203 & 0.348 \\ \hline \multirow{2}{*}{VGG19 [6]} & Yes & 16 & 0.982 & 0.062 & **0.995** & 0.008 \\  & No & 17 & 0.985 & **0.043** & **0.995** & **0.004** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the models proposed in this paper and existing deep learning models learned based on data with noise.