# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 5 Conclusions  
This study presents a novel multi-scale feature fusion residual attention model (MUSCA) for birdsong recognition based on audio spectrogram features. Leveraging a shallow residual network as the backbone and integrating both channel and spatial attention mechanisms, MUSCA effectively addresses the challenge of extracting meaningful information from complex, multi-dimensional birdsong signals.  
By fusing log-power spectrograms, log-Mel spectrograms and log-wavelet spectrograms, the proposed model significantly mitigates the information loss typically associated with single-scale feature extraction. The introduction of attention mechanisms further enhances the model's capacity to distinguish between spectrograms with similar shapes but different frequency distributions, overcoming key limitations of conventional CNNs such as translation invariance and parameter sharing.  
Extensive experiments conducted on a publicly available birdsong dataset validate the robustness and generalization ability of MUSCA across various spectrogram types. The model consistently outperforms classical single-scale architectures such as CNN, ResNet18 and ResNet50, especially under challenging acoustic conditions.  
Despite its promising performance, this study acknowledges several limitations. First, the manual annotation of birdsong data remains time-consuming and labor-intensive, posing challenges to the scalability of large-scale ecological research. Future work will focus on developing automated techniques for birdsong endpoint detection and species labeling to accelerate dataset construction and reduce human effort. Second, although down-sampling bird audio reduces computational load and training time, it may also result in the loss of harmonic information critical for accurate species identification. Balancing efficiency and acoustic fidelity will be an important consideration in future improvements. Lastly, the integration of multi-scale feature extraction and attention mechanisms, while enhancing recognition accuracy, inevitably increases the model's computational complexity. As a next step, we plan to explore model compression strategies and investigate lightweight alternatives, such as MobileNet-based attention modules, to reduce the computational footprint without compromising performance.  
Figure 9: The classification accuracy of the test set in each spectrogram. (**a**) Confusion matrix of log-power spectrogram; (**b**)confusion matrix of log-wavelet spectrogram; (**c**)confusion matrix of log-Mel spectrogram.  
**Author Contributions:** Conceptualization, L.G.; Data curation, L.G., W.L. and Z.W.; Funding acquisition, G.D., D.L., Y.Z. and Y.Y.; Investigation, L.G. and W.L.; Methodology, L.G., G.D., D.L. and Y.Z.; Supervision, G.D., D.L. and Y.Z.; Validation, D.L., Y.Z. and Y.Y.; Writing--original draft, L.G.; Writing--review and editing, L.G., G.D., D.L., Y.Z. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Agricultural Joint Fund of Yunnan Province under Grant no: 202301 BD070001-086, the Scientific Research Foundation of the Education Department of Yunnan Province, China under Grant no: 2022J0495, the National Natural Science Foundation of China under Grant no: 31860332, the National Natural Science Foundation of China under Grant no: 32360388, and Research on the Application of Multi-Target Swarm Intelligence Algorithms with the Multi-Modal in Biological Data.  
All datasets included in this work are public datasets, ensuring transparency and accessibility.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.