Bird Species Prediction Based on Voice Using CRNN

Abstract: Bird species recognition through sound is a crucial tool for biodiversity monitoring, enabling non-invasive, scalable insights into avian populations and their habitats. This project aims to develop a machine learning-based bird sound recognition system that identifies bird species from audio. The system processes audio inputs to extract relevant acoustic features, such as spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs), leveraging tools like LibROSA. A Convolutional Recurrent Neural Network (CRNN) hybrid model is trained on these features, utilizing its ability to capture spatial patterns within audio data, for accurate bird species prediction. The proposed system also integrates a web interface, allowing users to upload recordings and view prediction results in real-time. This interface facilitates broader accessibility, making it useful for both research and citizen science initiatives