Integrating Deep Learning Techniques for Wildlife Species Identification Using Vocalization Analysis

Abstract

This study introduces a novel approach to wildlife conservation by integrating deep learning techniques for species identification through animal vocalizations. Existing models face limitations in generalization, complexity, data imbalance, and computational complexity. Addressing these, the study employs innovative deep learning, pre-processing, and data augmenta-tion methods, enhancing representation and robustness. Major findings include successful pre-processing techniques like mel spectrogram transformation and various data augmentation methods. The model, trained on a large dataset, demonstrates superior performance, notably with Res Net152. This approach offers accurate, non-intrusive species identification, promising ad- vancements in wildlife monitoring for conservation and ecosystem management. In conclusion, deep learning-based approaches hold significant potential for enhancing wildlife conservation strategies and sustainable resource management.