## 4 Experiments and Result Analysis
### Ablation Experiments  
To evaluate the effectiveness of each module in the proposed model, ablation experiments were carried out. Table 6 shows the results of ablation experiments of the DBS-NET model with a different module. iDAFF (iterative dual-attention feature fusion), Linear Residual and Weighted Loss of the model are considered. The experimental results demonstrate that the performance of model progressively improves as additional modules are incorporated. Model 8 (with iDAFF, Linear Residual, and Weighted Loss) achieves the best performance across all metrics, with an accuracy of 97.54%, precision of 97.47%, recall of 97.23%, and F1-score of 97.22%. Compared with the baseline model (Model 1), accuracy was increased by 0.76%. Furthermore, the combination of iDAFF and Linear Residual (Model 5) improved the accuracy by 0.64%, while the model with Weighted Loss (Model 6) performed slightly worse. These results indicate that the integration of all modules plays a crucial role in enhancing the performance of DBS-NET. Additionally, eliminating the self-supervised branch decreases overall accuracy by 0.87% points and reduces F1-score by 0.66% points, indicating that the SimCLR-based branch plays a decisive role in final performance of DBS-NET, enchancing. To further illustrate the impact of the weighted loss, we drew radar charts to visualize the results, as shown in Figure 8. After applying the weighted loss, the model achieved significant improvements in precision, recall, and F1-score over different classes compared to without using weighted losses, especially for previously underperforming classes (e.g., \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Category** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & Aythya fuligula & 98.10 & 98.73 & 98.10 & 98.41 \\
2 & Elachura formosa & 1.00 & 1.00 & 1.00 & 1.00 \\
3 & Leidothrix lutea & 95.24 & 98.59 & 95.24 & 96.89 \\
4 & Perdrix perdix & 1.00 & 98.27 & 1.00 & 99.13 \\
5 & Epropnis zantholeuca & 99.37 & 1.00 & 99.37 & 99.69 \\
6 & Coturnix coturnix & 96.69 & 95.99 & 96.69 & 96.34 \\
7 & Asio otus & 98.57 & 95.17 & 98.57 & 96.84 \\
8 & Asio flammeus & 99.73 & 97.92 & 99.73 & 98.82 \\
9 & Grus grus & 96.05 & 97.33 & 96.05 & 96.69 \\
10 & Numenius phaeopus & 97.77 & 98.04 & 97.77 & 97.91 \\
11 & Gallus gallus & 99.51 & 99.51 & 99.51 & 99.51 \\
12 & Glareola maldivarum & 97.92 & 98.95 & 97.92 & 98.43 \\
13 & Larus canus & 94.24 & 97.04 & 94.24 & 95.62 \\
14 & Milvus migrants & 99.10 & 97.92 & 99.10 & 98.50 \\
15 & Haliaeleus albicilla & 1.00 & 98.19 & 1.00 & 99.09 \\
16 & Accipiter nisus & 97.65 & 99.05 & 97.65 & 98.35 \\
17 & Accipiter gentilis & 95.38 & 97.64 & 95.38 & 96.50 \\
18 & Falco tinnunculus & 91.93 & 96.73 & 91.93 & 94.27 \\
19 & Phasianus colchicus & 96.50 & 97.97 & 96.50 & 97.23 \\
20 & Lagopus muta & 95.34 & 97.35 & 95.34 & 96.34 \\
21 & Lyrurus tetrix & 98.30 & 97.47 & 98.30 & 97.88 \\
22 & Cygnus olor & 98.94 & 97.89 & 98.94 & 98.41 \\
23 & Cygnus cygnus & 98.67 & 97.37 & 98.67 & 98.01 \\
24 & Branta canadensis & 96.55 & 97.39 & 96.55 & 96.97 \\
25 & Anas platyhynchos & 93.22 & 95.65 & 93.22 & 94.42 \\
26 & Cettia cetti & 99.33 & 97.06 & 99.33 & 98.18 \\
27 & Acrocephalus arundinaceus & 97.25 & 95.93 & 97.25 & 96.59 \\
28 & Phyloscopus trochiloides & 95.00 & 95.00 & 95.00 & 95.00 \\
29 & Phyloscopus plumbeitarsus & 98.38 & 98.78 & 98.38 & 98.58 \\
30 & Francolinus pintadeanus & 95.65 & 1.00 & 95.65 & 97.78 \\ \hline \hline \end{tabular} Note: All the evaluation results use resnet18 as encoder.  
\end{table} 
Table 5: The performance of birdsong classification with different classification learning methods. Class 1 and Class 15). The smoother curves in the weighted loss chart indicate more balanced performance across multiple classes, mitigating the effects of class imbalance. In addition, the average accuracy of weighted loss increased by 0.12%, precision by 0.26%, recall by 0.39%, and F1-score by 0.25%.