• Ms. Darshana Manohar Wath
• Ms. Pranjali Prakash Bhopale
• Ms. Snehal Chandrakant Adole
• Ms. Prerana Rajesh Giri
• Dr. Avinash P. Jadhao

Bird species classification plays a critical role in ornithology, as it contributes to the understanding of biodiversity, species distribution, habitat suitability, and ecological dynamics. Accurately identifying bird species is fundamental for assessing the health of ecosystems and monitoring the impacts of environmental changes such as climate change, deforestation, and urbanization. However, manual identification of birds, especially in the wild, is a highly complex and error-prone task. Even seasoned ornithologists and bird watchers may struggle to differentiate between species due to similarities in physical features, background clutter, varying postures, or poor lighting conditions. The system is designed to assist amateur bird watchers, researchers, and conservationists in identifying bird species from real-time audio recordings and images captured in natural habitats. Birds of the same species may appear differently depending on their posture (e.g., perched, in flight, or partially hidden by foliage), and background noise often affects audio recordings, making species classification even more complex. To overcome these challenges, our model integrates pre-trained deep learning architectures— such as convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) or spectrogram-based CNNs for audio data. The proposed system not only contributes to biodiversity monitoring and wildlife conservation but also promotes citizen science by enabling wider public participation in ecological studies. Future work includes extending the dataset, improving noise handling in audio inputs, and deploying the model in a mobile-friendly application for on-the-go identification.

Ornithology, Bird Species Classification, Machine Learning, Deep Learning.

"“Bird Species Identification Using Computer Vision And Audio Analysis”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.k627-k640, April-2025, Available :http://www.jetir.org/papers/JETIR2504A80.pdf

"“Bird Species Identification Using Computer Vision And Audio Analysis”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppk627-k640, April-2025, Available at : http://www.jetir.org/papers/JETIR2504A80.pdf