## 4 Experiments and Discussion
### Experimental Methods  
This section describes the experimental design and experimental details. This paper proposed FEM-ATNN to improve the underwater acoustic target recognition model's accuracy, robustness and generalization. In addition, we verify the feasibility of applying the audio data pre-trained model to underwater acoustic target recognition. Due to the diversity in the speed of vessels, environment and navigation states, there are differences in ship radiated noise under different voyages. In short, the difference in radiated noise is closely related to the voyage. The difference in radiated noise at the beginning and end of a long-term voyage is greater than that in a short period. The difference in radiation noise between different voyages is more likely than that of the same voyage. For an intelligent system, the greater the difference between training and test data, the more generalized the model will be. Generally speaking, there are three ways to divide training data and test data: random segmentation, front/back segmentation, and different recording segmentation. Three types of division represent three different task difficulties, from simple to challenging. The method of evenly segmenting the recordings and randomly selecting the training \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Targets** \\ \hline Class-A & Fishing boats, trawlers, mussel boats, tugboats and dredgers \\ Class-B & motorboats, pilot boats, sailboats \\ Class-C & passenger liners \\ Class-D & ocean liners and ro-ro vessels \\ Class-E & ocean noise \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Target category on ShipsEar. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Classifier 1** & **Classifier 2** \\ \hline Fully Connected & FC (512,256) & FC (256,256) \\ Dropout & Rate = 0.5 & Rate = 0.5 \\ Fully Connected & FC (256,x) & FC (256,x) \\ LogSoftmax & Sm (x,x) & Sm (x,x) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classifiers are used in this paper. set and test set may obtain high accuracy, but it is too easy and cannot well evaluate the underwater acoustic target recognition algorithm because the training data and test data are highly similar in the near seconds. Therefore, this paper designs two classification tasks according to front/back segmentation and different recording segmentations based on ShipsEar, selects 88 recordings for experiments, and both tasks divide recordings into five categories according to Table 4. Task 1 is to construct a fourfold dataset by dividing each of the recordings into four pieces on average according to the time sequence. This division method can separate training data and test data to a certain extent, which is suitable for evaluating the fitting ability of the neural network model. Arranging each recording as training or test data in a 3-1 ratio is Task 2. Task 2 is difficult because the underwater acoustic target recognition algorithm should be able to identify the unknown voyages even if the specific target is not present in the training set. Task 2 is more suitable for evaluating the algorithm's generalization and practicability than Task 1. We use Network 1 in Task 1 and conduct Network 2 in Task 2. All data is downsampled to 16 kHz, and 3626 records of data are obtained through division and simple selection, and the duration of each data point is 3 s.  
Based on two classification tasks, this paper arranges four groups of results comparisons to evaluate the proposed method's performance. The first results compare the FEM-ATNN with multi-resolution STFT on task 2. The second experiment uses standard STFT as the primary feature to verify the performance of model-based transfer learning on task 1. The third experiment conducts the FEM-ATNN and model-based transfer learning in the same model, evaluates it on Task 1 and Task 2, and compares it with Mel filter bank energy (FBank), Mel frequency cepstral coefficients (MFCC) and linear frequency cepstral coefficients (LFCC).