## 1 Introduction  
Birds are one of the most widely distributed species, inhabiting almost all ecosystems, and their survival is an important indicator of global ecological health. However, the Global Red List of Birds indicates that 1445 bird species (12.95% of the total) are classified as Vulnerable (VU), Endangered (EN), or Critically Endangered (CR) [1]. Therefore, it is vital to protect birds. Birdsong is a primary means of communication among birds and is essential for the interaction within and between populations. Different species exhibit distinct song patterns. Analyzing the subtle features of birdsong patterns can yield information that cannot be obtained through images. In addition, the recordings of birdsong are not affected by the field of view or lighting conditions [2]. Thus, identification of bird species based on song is one of the most important methods in ecological monitoring and a vital component of habitat monitoring and protection for endangered species [3]. However, due to the diversity of bird species and the complexity of birdsongs, the annotation of bird audio data is time-consuming. Therefore, making use of existing annotated data to achieve accurate birdsong recognition remains a challenge. With the development of deep learning in the field of computer vision, a novel exploratory mechanism has been introduced to solve the challenges of birdsong recognition. At present, the birdsong classification technology mainly converts signals into spectral representation and carry out detailed analysis of the spectral features of birdsong [2]. Common spectral representations, such as Short-Time Fourier Transform (STFT) spectra, Mel spectrograms, and Mel Frequency Cepstrum Coefficients (MFCC), are used as inputs to convolutional neural network (CNN) models for birdsong classification. For example, [PERSON] et al. [4] combined features of Linear Prediction Cepstral Coefficients (LMs) and MFCC with a 3 DCCNN-LSTM model to classify four bird species, obtaining an average accuracy of 97.9%. [PERSON] et al. [5] combined Mel spectrograms, harmonic component spectrograms, and percussion component spectrograms with a CNN model, achieving an F1-score of 95.95% for the classification task of 43 bird species. [PERSON] et al. [6] introduced an improved Transformer model for recognizing MFCC spectrograms of 15 bird species with an accuracy of 93.57%. [PERSON] et al. [7] applied the wavelet transform to convert birdsongs into spectrograms, constructed a DR-ACGAN model, and incorporated a dynamic convolutional kernel into the classifier, achieving an accuracy of 97.60% with the VGG16 model. [PERSON] et al. [8] developed BirdNET, which used Mel spectrograms to successfully identify 984 bird species in North America and Europe, with an average accuracy of 0.791 in the high noise environments. These studies highlight the effectiveness of neural networks in the classification of birdsong spectrograms. However, most of them relied on supervised learning approaches and required a large amount of labeled training data. In addition, supervised learning methods are prone to generalization errors, spurious associations, and adversarial attacks [9]. With the advent of unsupervised learning [10; 11; 12; 13], supervised information can now be obtained from unlabeled data. Self-supervised learning [14; 15; 16; 17; 18; 19] is a subset of unsupervised learning, and contrastive learning [20; 21; 22; 23; 24; 25] is a branch of self-supervised learning [9]. Contrastive learning is a discriminative method whose aim is to cluster similar samples more closely in the representation space while dispersing dissimilar samples. Through this process, the model learns more discriminative features, enabling it to better distinguish different samples, and providing a solid foundation for solving downstream tasks.