## 3 Experiments and Analysis
### Experimental Analysis
#### 3.3.2 Experiment B: The Advantage of the ResNet18 Model  
This experiment is designed to compare the performance of the described classifier with other typical models. Different networks, named \"CNN-1, CNN-2, LSTM, CRNN, and ResNet18,\" are exploited for comparison. As shown in Table 4, CNN-1 and CNN-2 share the same network structure, while the features are MFCC and LM + MFCC + CCTZ, respectively. The first layer exploits 32 filters of the size of \(3\times 3\) with the activation function of the hyperbolic tangent function named \"tanh,\" followed by the \(2\times 2\) max-pooling processing. The second layer uses 64 filters of the size of \(3\times 3\) with the activation function of tanh, followed by max-pooling of \(2\times 2\). The last layer utilizes 128 filters of the size of \(3\times 3\) with the activation function of tanh, followed by max-pooling of \(2\times 2\) and batch normalization. Afterward, the fully connected dense layers consist of 1024, 128, and 5 nodes, respectively. The optimizer is Adam with a learning rate of 0.001 and a decay factor of \(1\times 10^{-6}\). The batch size and the number of epochs are set to be 128 and 50, respectively. With CNN-2 fed by the optimized feature of LM + MFCC + CCTZ, we can achieve the average accuracy of 0.906, which surpasses that of CNN-1 of 0.845. The LSTM network with the LM feature only achieves an average accuracy of 0.852. The first two LSTM layers have 256 and 128 outputs with batch normalization, respectively. The dropout is set to be 0.2. Afterward, the fully connected part consists of 256 and 5 nodes, respectively. The optimizer is Adam. As for the CRNN network, the first layer has 64 filters of the size of (5,5) and the regularization method is L2 with the lambda of 0.01. The activation function is ReLU. Batch Normalization is also used and the dropout rate is 0.25. The second and third layers are LSTM, which have 64 units and are regularized L2 with the lambda of 0.01. The dropout rate of recurrent is 0.5, the activation function is tanh, and the dropout rate is 0.25. The fourth and fifth layers are time distributed layers with 128 and 64 nodes. The activation functions are ReLU and the dropout is 0.25. The sixth layer is a dense layer with 5 nodes. The optimizer is Adam with a learning rate of 0.001 and a decay factor of \(1\times 10^{-6}\). Such a method could achieve an average accuracy of 0.885. From Table 4, we could have a better insight into the performance from the detailed accuracy. The ResNet18 with the proposed feature extraction method has an advantage over other methods. Besides, we also compare the proposed method with other methods. The baseline design is based on the study [16], which shows that using the basic machine learning method, the accuracy is 0.754. Besides, the accuracy achieved by the ResNet18 model as well as that achieved by other state-of-the-art approaches of RBM + BP [18] and RSSD [4] described in the literature are presented in Table 5. Our method achieves an accuracy of 0.943. These results indicate that our ResNet18 has achieved significant improvement in UATR with three-dimensional features as input. Given that the split of the dataset could be different for different methods, the comparison is not rigorous. However, to our knowledge, the best results are obtained by the proposed method for ShipsEar from the accuracy perspective. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **CNN-1** & **CNN-2** & **LSTM** & **CRNN** & **ResNet18** \\ \hline Feature & MFCC & LM + MFCC + CCTZ & LM & LM + MFCC + CCTZ & LM + MFCC + CCTZ \\ Class A & 0.926 & 0.973 & 0.831 & 0.936 & 0.964 \\ Class B & 0.806 & 0.902 & 0.805 & 0.867 & 0.947 \\ Class C & 0.744 & 0.853 & 0.869 & 0.832 & 0.915 \\ Class D & 0.881 & 0.919 & 0.862 & 0.908 & 0.950 \\ Class E & 0.832 & 0.897 & 0.875 & 0.876 & 0.938 \\ Average & 0.845 & 0.906 & 0.852 & 0.885 & 0.943 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of different models.