Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective

Abstract

The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates hierarchical multilevel labels for each bird. PPNN uses a hierarchical semantic embedding framework to capture feature information at different levels. Attention mechanisms are employed to extract and select common and distinguishing features, thereby improving classification accuracy. We also propose a path correction strategy to rectify inconsistent predictions. Experimental results on bird acoustic datasets demonstrate that PPNN outperforms current methods, achieving classification accuracies of 90.450%, 91.883%, and 89.950% on the Lishui-Zhejiang Birdsdata (100 species), BirdCLEF2018-Small (150 species), and BirdCLEF2018-Large (500 species) datasets respectively, with the lowest hierarchical distance of a mistake across all datasets. Our proposed method is applicable to any bird acoustic dataset and presents significant advantages as the number of categories increases.