## 1 Introduction
The existing contrastive learning methods include Simple Contrastive Learning Representation (SimCLR) [17], Momentum Contrast (MoCoV2) [26], Swapping Assignments between Views (SwAV) [19], Bootstrap Your Own Latent (BYOL) [14], and Siamese Network for Contrastive Learning (SimSiam) [18]. The fine-tuned classification accuracy rates of these methods on ImageNet are shown in Figure 1. As shown in Figure 1, the accuracy of existing contrastive learning approaches is very close to that of supervised learning, indicating that these unsupervised feature extraction techniques are effective. SimCLR is a classic algorithm for contrastive learning with simple structure, clever design and high performance.It has achieved remarkable success in computer vision. [PERSON] [27] used unlabeled data to train a feature extraction network with SimCLR, and then added a linear classification layer for supervised training and fine-tuning with a small amount of labeled data, achieving remarkable results in the intrusion detection for industrial control systems. [PERSON] et al. [28] pre-trained SimCLR on unlabeled plant image data and fine-tuned the pre-training model on labeled plant disease samples, obtaining an accuracy comparable to that of supervised learning. [PERSON] et al. [29] combined SimCLR with the MetaFormer-2 model to classify snakes at a fine-grained level, with an accuracy of 83.8%. [PERSON] et al. [30] pre-trained SimCLR on a dataset of chest digital radiography (DR) images and used it as the backbone of a fully convolutional one-stage (FCOS) object detection network to identify rib fractures from DR images. SimCLR leverages a large amount of unlabeled data to learn general data representations through data augmentation and transformations and then updates the network based on the InfoNCE loss (Normalized Temperature-Scaled Cross-Entropy Loss) [31,32]. This learned knowledge is then transferred to smaller models for specific downstream tasks. The fine-tuned downstream tasks in these studies achieved an accuracy rate comparable to that of supervised learning, further demonstrating the powerful feature representation capability of SimCLR contrastive learning. However, this two-stage training process increases complexity, is time-consuming, requires additional resources, and poses challenges for optimization and debugging. [PERSON] [33] further demonstrated that although self-supervised learning has been successful in several downstream tasks. However, if the model is fine-tuned using only labeled data from downstream tasks, its generality and applicability may not be fully realized. In addition, existing supervised learning methods still require a significant amount of time [28]. Therefore, we address the challenge of utilizing the representation capability of self-supervised learning to reduce training time while achieving better accuracy. In this study, we propose a Dual-Branch Supervised and Unsupervised Learning Network (DBS-NET) is proposed for Birdsong Classification, which employs a weight assignment strategy to balance the losses of the two branches, and enhance the feature generalization ability of the backbone network. To verify the effectiveness of DBS-NET model, experiments are carried out on the self-built 30-class and Birdsdata datasets. The main contributions of this paper are summarized as follows:  
1. A Dual-Branch Network (DBS-NET) combining supervised and self-supervised learning is proposed for birdsong classification. Using a weight allocation strategy, unsupervised and supervised feature extraction are integrated into one framework for joint training, which effectively balances the contributions of the two branches. 2. An enhanced backbone based on an iterative dual-attention feature fusion module (iDAFF) is constructed, and an enhanced linear residual classifier is designed to further improve the classification capability of the model. 3. Weighted loss for A class imbalance weighted loss function is designed to calculate the weight of each category according to its frequency in the dataset.