## 3 Experiments and Analysis
### Experimental Result  
The proposed method is verified by a computer with four GPUs of Nvidia GeForce RTX 2080 Ti and Core i7-6900K CPU. Furthermore, the deep learning framework of the proposed model is implemented using Keras 2.2.4 with TensorFlow 1.12.0 as a backend. When training the model, the batch size and the maximum number of epochs (each epoch includes one training cycle on all training data) are set to be 128 and 200, respectively. To accelerate the training process, the early stopping strategy is that the training will be stopped if the validation loss is reduced by larger than 0.00005 in 20 successive epochs. Besides, the adaptable strategy of learning rate is adopted, where the initial value is 0.001, and the value will be 60% of the former value every 20 epochs. Using such a strategy, the practical number of epochs used is 88. As shown in Figure 4(a), the training loss and validation loss are rapidly reduced within about 20 epochs, and they are gradually decreased without overfitting due to the fact of the design of the adaptable learning rate. Meanwhile, Figure 4(b) also shows that the accuracy is improved at a varied speed. Only \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class A** & **Class B** & **Class C** & **Class D** & **Class E** \\ \hline Background noise & \begin{tabular}{c} Fishing boats, trawlers, \\ mussel boats, tugboats, \\ and the dregler \\ \end{tabular} & \
\begin{tabular}{c} Motorboats, pilot boats, \\ and sailboats \\ \end{tabular} & Passenger ferries &
\begin{tabular}{c} Ocean liners and \\ ro-ro vessels \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A detailed description of the five classes. small improvements are obtained after the turn and the best accuracy of the validation data is 0.946. We can observe that Figure 5a, b describes the same process. We evaluate our model of ResNet18 with the three-dimensional feature. Twenty percent of the data is used as the validation set and 10% of the data is used as the test set, while each network is trained with 70% of the dataset with the augmentation. Table 2 depicts the detailed results of the classification system at the aspect of precision, recall, and f1-score. It is clear that all the accuracy of the recognition of each class is higher than 0.90 and the average precision or recall or f1-score is 0.943, where the support denotes the number of each test class. For the convenience of comparison, classifier performance is measured using the classification accuracy, defined as the average precision. The ability of the described classifier to identify different vessels is indicated by the fact that there is no confusion between background noise class E and four vessel classes A-D [16]. The vessel classes with the best results are A (background noise) and B (fishing boats, trawlers, mussel boats, tugboats, and the dredger), with classification rates of 0.970 and 0.958, respectively. The poorest results are obtained for C (motorboats, pilot boats, and sailboats). Although the acoustic dataset contains high background noise in shallow water, the overall performance is still satisfactory. Figure 6 shows the confusion matrix of the proposed method on the ShipsEar dataset, where classes 0-4 denote classes A-E, respectively. Values along the diagonal indicate the number of samples classified correctly for each specific class. It shows that class C is the hardest class for the proposed classifier, while all other classes are well separated. It is worth noting that the parameters such as the number of filters, filter size, and the number of layers and the hyperparameters for training such as the batch size, the initial learning rate, and the patience in early stopping are optimal choices according to the train and validation process in the experiment. Selecting other parameters is also feasible, however, using different parameters could encounter the performance loss and using different hyperparameters does not have significant impact but exhibit slightly inferior performance.