## 4 Experiments and Result Analysis
### Model Performance Experiments  
To demonstrate the effectiveness of combination of self-supervised representation learning and supervised classification learning, the comparative experiments are conducted on the self-built dataset. The parameter settings for different models are shown in Table 3. The experimental results are listed in Table 4. DBS-NET performs well in all four metrics. For example, the accuracy of DBS-NET is 1.73% higher than SCL and 1.78% higher than than of SSCL. DBS-NET completes training in an average of 13.86 h, whereas SSCL requires 9.958 h for pre-training and 7.597 h for fine-tuning, totaling 17.555 h. Thus, compared with SSCL, DBS-NET not only improves classification accuracy but also markedly shortens training time. Table 5 shows the training performance of the proposed model for each category. As seen in the table, the accuracy rates for the two categories, Falco intinunculus and Anas platyrhynchos, are relatively low. This may be due to the small sample size of these two categories, which makes it difficult to learn effective representations. To better understand the classification results, t-SNE (t-distributed Stochastic Neighbor Embedding) [39] was used to visualize the output features of SCL, SSCL, and DBS-NET by downscaling the high-dimensional features to two dimensions. The results are shown in Figure 7. Subfigure \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Parameter Settings** & **Value/Type** \\ \hline \multirow{4}{*}{DBS-NET} & Learning rate & 0.0005 \\  & Epoch & 200 \\  & Batch size & 64 \\  & Lr\_scheduler & ReduceLROnPlateau \\  & Optimizer & Adam \\ \hline \multirow{4}{*}{SCL} & Learning rate & 0.001 \\  & Epoch & 100 \\  & Batch size & 64 \\  & Lr\_scheduler & CosineAnnealingLR \\  & Optimizer & Adam \\ \hline \multirow{4}{*}{SSCL} & Learning rate & 0.0005(pre)/0.001(fine) \\  & Epoch & 200(pre)/50(fine) \\ \cline{1-1}  & Batch size & 64(pre)/32(fine) \\ \cline{1-1}  & Lr\_scheduler & ReduceLROnPlateau(pre)/StepLR(fine) \\ \cline{1-1}  & Optimizer & Adam \\ \hline \hline \end{tabular} Note: Self-Supervised Classification Learning (SSCL) is divided into two stages: pre-training (pre) and fine-tuning (fine).  
\end{table} 
Table 3: Hyperparameters of experiments. (a) presents the results of SCL visualization. The distribution of classification features is relatively scattered, and there is obvious overlap between many categories. However, it is still possible to observe that the feature points of some categories form distinct clusters. Subfigure (b) shows the visualized results of SSCL. Compared with SCL, the feature point distribution is more compact, indicating that the model can better distinguish features from different categories. However, some category clusters still contain data from other categories, suggesting that the model still struggles with distinguishing similar features. Subfigure (c) is the t-SNE visualization of DBS-NET. The output features of DBS-NET are highly clustered in the t-SNE space, with a clear separation between different categories. This indicates that DBS-NET can capture the similarities and differences of birdsong features more effectively, so that similar features are still clustered together after dimensionality reduction. However, there is still some overlap between Class 23 and Class 24, and the data of these classes are mixed with other classes. This may be due to misclassifications of the model, but it does not affect the superior performance of our model.