## 4 Experiments and Result Analysis
### Generalization Experiments  
To evaluate the generalizability of the proposed model, we conducted experiments on three datasets: BirdSong30, Birdsdata, and BirdSong16. BirdSong30 serves as the primary dataset for this study. Birdsdata is a public dataset containing 20 bird species and 14,311 audio clips (each clip is normalized and trimmed to 2 seconds). BirdSong16 contains 16 categories and 271 recordings. Detailed information of datasets are provided in Table 8. Table 9 shows the comparison results, indicating that the proposed model performs well on all three datasets. The model achieves accuracy, precision, recall, and F1-score above 97% on BirdSong30 and Birdsdata, with slightly lower (but still competitive) results on BirdSong16. These findings suggest that the model shows good stability and generalization on different datasets. To further validate the generalization ability of our proposed model, experiments on different backbone networks were conducted. As shown in Table 10, our model achieves consistently high performance on various backbones and remains competitive on all metrics. This demonstrates the applicability and flexibility of the framework. Notably, ResNet18 outperforms the other networks, with a relatively low computational complexity (4.535 GFLOPs) and a moderate number of parameters (12.603M). Although ResNet34 and ResNet50 provide slightly lower performance, their increased number of parameters  
Figure 10: Unsupervised branch contribution to classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Categories** & **Samples** & **Training** & **Testing** & **Available** \\ \hline BirdSong16 & 16 & 15,426 & 12,332 & 3094 & [[https://www.xeno-canto.org/](https://www.xeno-canto.org/)]([https://www.xeno-canto.org/](https://www.xeno-canto.org/)) (accessed on 8 April 2024) \\ Birdsdata & 20 & 14,311 & 11,448 & 2862 & [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) efmp3 mQ7 Vo0]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) efmp3 mQ7 Vo0) \\  & & & & 19a6 zdZbYTFhne6 Pjytew/view?usp=drive_link (accessed on 28 May 2024) \\ BirdSong30 & 10 & 8335 & 6668 & 1667 & \\ \hline \hline \end{tabular} Note: Some studies selected Birdsdata’s 10 species, and for a fair comparison, we also used that dataset.  
\end{table} 
Table 8: Description of datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BirdSong30 & 97.54 & 97.47 & 97.23 & 97.22 \\ Birdsdata & 97.09 & 97.25 & 97.11 & 97.17 \\ BirdSong16 & 97.01 & 97.05 & 96.74 & 96.88 \\ \hline \hline \end{tabular} Note: This set of experiments was conducted using the infoNCE Loss function.  
\end{table} 
Table 9: Generalization experiments of DBS-NET on different datasets. and computational costs (6.389 GFLOPs and 8.887 GFLOPs, respectively) make them less efficient in practice. The experimental results indicate that the shallower layers performs better on the birdsong dataset. This can be attributed to the nature of spectrogram recognition tasks, where local texture and frequency band information are crucial, making deeper network architectures unnecessary. The shallower network can effectively extract the basic discriminative features for classification. Moreover, ResNet18 retains high-resolution feature maps in its early stages, providing a distinct advantage for tasks that require fine-grained information extraction. [PERSON] et al. [40] also pointed out that models with fewer layers perform better on sound-related tasks. Therefore, in this paper, ResNet18 was chosen as the backbone network. Table 11 compares the classification accuracy of ResNet18, ResNet34, and ResNet50 under SCL, SSCL, and the full DBS-NET. Despite having the fewest parameters, ResNet-18 delivers the best performance in all three settings--95.94% (SCL), 95.83% (SSCL) and 97.54% (DBS-NET).