## 3 Methods
### Iteration Dual Attention Feature Fusion Module  
In the residual blocks, skip connections are usually used to fuse the original features \(X\) with the convolved output \(X^{\prime}\). These connections provide an uninterrupted alternative path for the gradient during the backpropagation process, making the gradient flow smoother and mitigating the problem of gradient vanishing. However, traditional feature fusion methods, such as element-wise addition or concatenation, often lead to information redundancy and fail to effectively capture contextual relationships between features.  
To address this issue, we refer to the work of [PERSON] et al. [38], proposing Multi-Scale Channel and Spatial Attention Module (MS-CSAM), dual-attention feature fusion (DFF), and iterative dual-attention feature fusion (iDAFF), as shown in Figure 5. In our MS-CSAM, a multi-scale feature extraction mechanism is introduced to combine maximum pooling, average pooling, and global average pooling to capture features at different scales. This is especially beneficial for tasks with significant differences in time-frequency features, such as birdsong classification, and can greatly improve the robustness and generalization ability of the model. In addition, MS-CSAM employs light convolution to reduce computational overhead while maintaining strong feature extraction capabilities to capture the fine-grained features and ensure efficient model performance. The fusion process is described as follows.  
We define two input features \(X,Y=C\times H\times W\) in ResNet skip connection. \(X\) represents the original feature and \(Y\) denotes the residual feature learned from the ResNet block. Based on the multi-scale channel attention module M, Dual-Attentional Feature Fusion (DAFF) can be formulated as in Formula (7).  
\[\mathbf{Z}=\mathbf{M}(\mathbf{X}\uplus\mathbf{Y})\odot\mathbf{X}+(1-\mathbf{M} (\mathbf{X}\uplus\mathbf{Y}))\odot\mathbf{Y} \tag{7}\]  
Figure 4: Data augmentation. During image augmentation, each method is randomly applied with a probability of 0.5. An image can be augmented by one or more methods. These augment methods include affine transformations, perspective transformations, contrast changes, Gaussian noise, hue/saturation changes, and blurring.  
In Formula (7), \(Z\) refers to the fusion feature, \(\uplus\) denotes the initial feature fusion, and the sum of the elements is used for calculation. DAFF is as shown in Figure 5b, and the dashed line represents \(1-M(X+Y)\). The fusion weight \(M(X\uplus Y)\) consists of real numbers between 0 and 1, as does \(1-M(X+Y)\), which allows the network to choose between X and Y or average them.  
The initial feature fusion is replaced by an DAFF module to form an iterative dual-attention feature fusion (iDAFF). Hence, \(M(X\uplus Y)\) can be re-expressed as in Formula (8). This iterative approach aims to optimize the quality of feature fusion through continuous improvement and adjustment so as to provide a more accurate and reliable data basis for subsequent analysis and application.  
\[\mathbf{X}\uplus\mathbf{Y}=\mathbf{M}(\mathbf{X}+\mathbf{Y})\odot\mathbf{X}+ \left(1-\mathbf{M}(\mathbf{X}+\mathbf{Y})\right)\odot\mathbf{Y} \tag{8}\]  
The traditional residual connections in the backbone network are replaced by the iDAFF mechanism. It combines local and global attention, and uses feature weighting for fusion. This approach improves the richness and expressiveness of feature representation, and maintains efficiency in terms of parameters and computational complexity. In addition, it significantly improves the performance of the network in handling complex tasks.