# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 3 Experiment and Result Analysis
### Experimental Environment and Performance Evaluation  
The experimental setup involved a desktop computer equipped with 128 GB of memory, a 16-core 32-thread CPU operating at a main frequency of 3.40 GHz and a NVIDIA GeForce RTX 4090 24 G GPU. The experiments were conducted on a 64-bit Ubuntu operating system. Anaconda3, Py Charm 2023.3.2 and Python 3.10 are used as deep learning platforms. In this study, binary cross-entropy loss is introduced on the multi-scale features of each layer to achieve more fine-grained supervision constraints. The final total loss consists of two parts: the first part is the sum of the binary cross-entropy loss calculated between each layer of features after full connection and category multi-label coding, which is not used in inference; the second part is the standard multi-classification cross-entropy loss calculated after these fully connected features are aligned and fused by the multi-scale feature fusion module described in Section 2.2.2. The final total loss is a weighted combination of the above two. The inference stage only relies on the fusion features output by the multi-scale feature fusion module, and the final multi-classification results are generated through the  
Figure 8: Statistical charts for each dataset. softmax layer. In order to comprehensively measure the performance of the model in each category, we calculate the evaluation indexes, such as precision and recall, for each category. The design aims to introduce continuous supervision signals for each layer of the feature extraction process so as to guide the model to learn more discriminative semantic representations at different scales. At the same time, the structure helps to enhance the gradient flow in the back propagation process, accelerate the convergence process of the model and improve the stability of training. The birdsong classification task in this study is a multi-classification problem involving 20 species of birds. The precision, recall and F1 values are calculated on the basis of each class and averaged using the macro-averaging method to ensure equal weights in all classes. The performance of the proposed birdsong classification model is evaluated by accuracy, precision, recall and F1 score [19]. The definition of each evaluation index is shown in Table 1.  
In multi-classification, when focusing on a specific category A, the sample whose true label is A is called a positive example of the category. On the contrary, the real label is not all the samples of category A, which is called the negative example of the category, that is, the samples of the 'other categories'. Accuracy is used to measure the performance of recognition models and indicate the proportion of the number of correct samples predicted by the model to the total number of predicted samples:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{9}\]  
Precision is judged based on the prediction results:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
Recall is judged based on the actual samples:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
F1 score is the harmonic mean of precision and recall used to balance these two metrics. The expression is as follows:  
\[\text{F1}-\text{score}=(1+\beta)\times\frac{\text{Precision}\times\text{ Recall}}{\text{Precision}+\text{Recall}} \tag{12}\]  
TP, TN, FP and FN, respectively, represent true positives, true negatives, false positives and false negatives. The F1 score is a metric used to evaluate the accuracy of a model. Precision indicates the proportion of true positive samples among all samples classified as positive. Recall denotes the proportion of true positive samples among all actual positive samples.