Search Results (1)

Birdsong classification plays a crucial role in monitoring species distribution, population structure, and environmental changes. Existing methods typically use supervised learning to extract specific features for classification, but this may limit the generalization ability of the model and lead to generalization errors. Unsupervised feature extraction methods are an emerging approach that offers enhanced adaptability, particularly for handling unlabeled and diverse birdsong data. However, their drawback may bring additional time cost to downstream tasks, which may impact overall efficiency. To address these challenges, we propose DBS-NET, a Dual-Branch Network Model for birdsong classification. DBS-NET consists of two branches: a supervised branch (Res-iDAFF) and an unsupervised branch (based on a contrastive learning approach). We introduce an iterative dual-attention feature fusion (iDAFF) module in the backbone to enhance contextual feature extraction, and a linear residual classifier is exploited further improve classification accuracy. Additionally, to address class imbalance in the dataset, a weighted loss function is introduced to adjust the cross-entropy loss with optimized class weights. To improve training efficiency, the backbone networks of both branches share a portion of their weights, reducing the computational overhead. In the experiments on a self-built 30-class dataset and the Birdsdata dataset, the proposed method achieved accuracies of 97.54% and 97.09%, respectively, outperforming other supervised and unsupervised birdsong classification methods. Full article