## 4 Experiments and Discussion
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fold**} & **Initialization** & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Accuracy**} \\  & **Mode** & & & & \\ \hline \multirow{3}{*}{1} & Random & 0.875 & 0.888 & 0.878 & 0.870 \\  & Pre-trained & 0.908 & 0.909 & 0.908 & 0.901 \\  & Random & 0.962 & 0.955 & 0.958 & 0.958 \\  & Pre-trained & 0.964 & 0.956 & 0.960 & 0.958 \\  & Random & 0.941 & 0.940 & 0.941 & 0.941 \\  & Pre-trained & 0.950 & 0.951 & 0.950 & 0.944 \\  & Random & 0.886 & 0.881 & 0.881 & 0.875 \\  & Pre-trained & 0.912 & 0.919 & 0.915 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition results of model based transfer learning. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline STFT-512 & 0.684 & 0.707 & 0.690 & 0.748 \\ STFT-1024 & 0.720 & 0.732 & 0.725 & 0.755 \\ STFT-2048 & 0.739 & 0.761 & 0.746 & 0.780 \\ STFT-4096 & 0.671 & 0.689 & 0.656 & 0.691 \\ STFT-8192 & 0.650 & 0.689 & 0.654 & 0.680 \\ FEM-ATNN & 0.787 & 0.816 & 0.797 & 0.839 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition results of the FEM-ATNN and the multi-resolution STFT. water acoustic target recognition network. The simple method effectively improves the recognition performance. The previous experiments proved the effectiveness of the proposed method and the feasibility of model migration, but the experiments were evaluated independently. Experiment 3 was devoted to the model-based transfer to the FEM-ATNN-based model (FEM-ATNN-trans) and conducted experiments on Task 1 and Task 2. In addition, the STFT is only a fundamental time-frequency analysis method used in Experiment 1. Experiment 3 added other mainstream time-frequency analysis methods to the comparison, including MFCC, LFCC and FBank. The backbone of those methods is consistent with the FEM-ATNN-based model according to Task 1 and Task 2. The details of those features are described in the second paragraph of the Experimental Method section. The results are shown in Table 7.  
For Task 1, fold 1 and fold 2 are selected for the experiment because they are the representative subtasks in Task 1, according to challenges. They represent the most challenging subtasks and the simplest subtasks in Task 1, as shown in Experiment 2. The test data of fold 1 is in the front of each recording, and the test data of fold 2 is in the middle. The difference between the test data and the training data of fold 2 is smaller than that of fold 1. Although the subtask based on fold 2 is easier to achieve high accuracy than the subtask based on fold 1, it is still meaningful to experiment on the subtask based on fold 2. An overfitting algorithm may achieve high accuracy in simple tasks and show decadence in challenging tasks. Conversely, the model with solid generalization may perform both on complex and simple tasks because it has learned the high-level representation that can distinguish categories. As shown in Table 7, the FEM-ATNN-trans is superior to other methods in all tasks, especially in Task 2. The challenge of Task 2 is that it requires the algorithm to learn adequate generalization information from training data, which must meet the needs of the underwater acoustic target recognition task. In addition, the accuracy of the FEM-ATNN-trans is 3.9% higher than that of the FEM-ATNN in Table 5, which further verifies the effectiveness of the proposed method and model-based transfer learning. Furthermore, we compare the FEM-ATNN-trans with other existing methods that have used the ShipsEar dataset, as shown in Table 8. Accuracy is used as a comparative measure because most papers use it to compare their results. The baseline is a machine learning method proposed in the ShipsEar research [36] with an accuracy of 75.4%. The accuracy of our method is the result of the representative subtask of Task 1, according to Table 7.