International Journal of Scientific Research and Engineering Development

Birds are essential indicators of ecological balance and biodiversity, making their monitoring vital for environmental research and conservation. Traditional bird identification methods, such as visual surveys and manual recognition of calls, are often limited by human expertise, time, and environmental noise. Recent advancements in artificial intelligence have enabled automated systems to recognize bird species through their vocalizations with higher accuracy and efficiency. This project, titled “Bird Audio Classification using Deep Learning,” proposes an intelligent framework that analyzes acoustic recordings to identify bird species. The system employs preprocessing techniques, including noise reduction and segmentation, followed by extraction of acoustic features such as MelFrequency Cepstral Coefficients (MFCCs), chroma features, and spectrogram representations. For classification, advanced deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) architectures—are applied to capture both spectral patterns and temporal dynamics of bird vocalizations. Trained on benchmark datasets such as BirdCLEF and open-source repositories, the system demonstrates strong robustness against pitch variations, background noise, and species overlap.