Specimen Detection Using Voice and Footprint Images in Deep Learning

Artificial Intelligence (AI) is a rapidly evolving domain that enables machines to simulate human intelligence, including perception, decision-making, and learning. Within AI, deep learning—a subset of machine learning—has revolutionized pattern recognition tasks by leveraging neural networks to automatically extract and learn features from complex data. In the context of biodiversity research and wildlife monitoring, AI plays a crucial role in identifying species through non-invasive means. Our project harnesses deep learning techniques to identify bird and animal species based on two key biometric inputs: voice (audio) and footprint images. This dual-modal approach enhances accuracy and reliability, enabling automated, scalable, and efficient species recognition for conservation, ecological studies, and environmental monitoring.