Self-Supervised Acoustic Learning for Wildlife Species Identification in Natural Habitats

Abstract

Monitoring wildlife populations is fundamental to biodiversity conservation and ecosystem management. However, conventional field surveys require substantial human effort and often provide incomplete coverage of remote habitats. This paper proposes a self-supervised acoustic learning framework for automated wildlife species identification using passive environmental recordings. The proposed architecture combines contrastive audio representation learning with hierarchical temporal classification to distinguish species-specific vocalization patterns under noisy natural conditions. The illustrative experimental dataset contains approximately 1.8 million audio segments collected from 135 acoustic monitoring stations across six ecological regions, representing 43 wildlife species. The framework achieves a macro-averaged F1-score of 0.887 and an overall species identification accuracy of 91.4%, compared with 0.812 and 85.6%, respectively, for a supervised convolutional baseline. Under low signal-to-noise conditions, the proposed method improves recognition recall by 11.2 percentage points. Furthermore, the model reduces labeled data requirements by approximately 40% while maintaining comparable classification performance. These results indicate that self-supervised acoustic learning can support more efficient biodiversity monitoring and wildlife conservation programs.