## 4 Experiments and Discussion
### Experimental Results and Discussion  
This paper proposed a time-frequency analysis method based on neural networks, and our calculation process and implementation were derived from the original Fourier transform. Therefore, the first experiment in this paper compares the proposed method with the short-time Fourier transform method to consider the advantages of the proposed method. The STFTs with different parameters carry different information because STFTs with different parameters have different frequencies and time resolutions. It can be predicted that different pieces of information will lead to different final recognition performances. Therefore, this paper selects the STFT of a series of typical parameters for comparison to evaluate the performance of the proposed method. Experiment 1 is carried out on the most challenging Task 2 to evaluate the generalization of various methods. The STFT with multi-resolutions as the primary feature is extracted to the same backbone network, and the classification performance of these primary features is compared with the model based on the FEM-ATNN. The window length of STFTs is from 512 to 8192, and the hop length is set to half of the window length. Table 5 shows the result of experiment 1. The STFT with different resolutions shows different performances in Experiment 1. Among them, the STFT has the best performance, with a window length of 2048. Its accuracy rate reaches 78.0%, which is significantly higher than others, especially compared with the window lengths of 4096 and 8192. The accuracy of the FEM-ATNN reaches 83.9%, which is 9.1%, 8.4%, 5.9%, 14.8% and 15.9% higher than the STFT with windows lengths from 512 to 8192, respectively. Experiment 2 verifies the feasibility of transferring the model, which is pre-trained on the large-scale audio pattern recognition data, to the underwater acoustic target recognition task. The advantage of transfer learning is to transfer knowledge from other fields to the current field. For a simple task with little difference between training data and test data, the effect is often not noticeable. Therefore, we not only use tasks with differences between training data and test data but also evaluate the boundary performance of model-based transfer when the training data and test data are similar. Task 1 provides a variety of test data and training data combinations, so we conduct experiments on Task 1 to facilitate a complete evaluation of the algorithm's performance. Task 1 divides each recording into four folds in chronological order. The differences between different folds and folds are not consistent. In other words, it is more challenging to use the head fold as the test set than the middle fold as the test set because the test data cut from the middle of the recordings is more similar to the surrounding training data. In order to evaluate it objectively, we used standard STFT as the primary feature in this experiment to compare the performance between the pre-trained and the random models. STFT-2048 is input into the backbone network as the primary feature because it is performed best in Task 2. The results of Experiment 2 are shown in Table 6.  
Table 6 shows the performance of the backbone network using pre-trained model parameter initialization and random initialization. The results show that model-based transfer learning can improve performance most of the time. The pre-trained model parameters can be used to initialize the backbone of the underwater acoustic target recognition model when the backbone is pre-trained on large-scale audio data unrelated to underwater acoustics. It is worth noting that the number of layers of the backbone used for underwater acoustic target recognition is different from that of the pre-trained backbone. We extract some layer parameters from the pre-training model to initialize all the layer parameters of the underwater acoustic target recognition backbone. That is, partial layer parameters are extracted from the pre-training model to initialize all layer parameters of the under