Optimizing avian species recognition with MFCC features and deep learning models

The rapid reduction in bird populations and the imminent prospect of avian extinction have profound effects on global ecosystems, putting vital ecological services and processes in jeopardy. Finding endangered bird species is a major problem for scientists, making it more difficult to come up with practical conservation plans. In order to meet this need, we provide an integrated system that combines MFCC-based feature extraction with state-of-the-art deep learning models CNN, LSTM, and VGGish to accurately identify bird species from audio recordings. Our method makes use of each model’s special strengths: VGGish represents extensive audio features, LSTM handles temporal dependencies, and CNN handles spatial hierarchies. Our framework seeks to improve species categorization efficiency and accuracy by utilizing these cutting-edge methods, supporting conservation efforts, and reducing the negative ecological effects of rapid population reduction. We work to provide ornithologists and conservationists with the resources they need to protect biodiversity and maintain the integrity of our ecosystems by continuously collecting data and disseminating information.