CNN-Faiss Pipeline for High Performance Animal Vocalization Retrieval and Classification

Aim: To use deep learning methods for better animal vocalization classification and wildlife monitoring, such as autoencoder-based analysis, Faiss, animal2vec, and pretrained CNNs. Materials and Methods:The study analyzed and classified animal noises using bioacoustic data, autoencoderbased approaches, dual audio recording systems, self-supervised transformers (animal2vec), pre-trained CNNs, Faiss for similarity search, and ensemble methods. Group 1: The existing method ($\mathbf{C}$) analyzes animal vocalizations using spectrogrambased features and Mel-Frequency Cepstral Coefficients (MFCCs), with an accuracy of 85.3%, precision of 76.4%, recall of 72.1%, and an F1 score of 83.2%. Group 2: The proposed technique (Intervention) uses a multi-model approach that enhances accuracy by $\mathbf{9 5. 0 \%}(\mathbf{9. 7 \%}$ increase), precision by $\mathbf{8 7. 8 \%}$ ($\mathbf{1 1. 4 \%}$ increase), recall by $\mathbf{8 9. 5 \%}$ ($\mathbf{1 7. 4 \%}$ increase), and an F1 score by 88.6% (5.4 increase) by combining pre-trained CNNs with Faiss for similarity search. This improved performance shows notable progress in applications for species identification and real-time wildlife monitoring. Result: Using pre-trained models and ensemble approaches, the strategy increased the accuracy of (98.0%) classifying both common and unusual animal vocalizations, improved noise suppression, and was more adaptable to new species. Conclusion: The deep learning techniques showed scalability, efficiency, and adaptability, which made them an effective tool for species identification and wildlife monitoring in a variety of environments.