Wings of Warning: Bird Song Forecasting

Birds are highly sensitive to environmental changes, which alters their vocalizations in response to natural disasters, climate variations, and human disturbances. Our project, "Wings of Warnings: Bird Song Forecasting," mainly focuses on analyzing bird songs to detect early warning signals of environmental changes. By using deep learning techniques and bio-acoustic analysis, it classifies bird vocalizations to determine whether they indicate an impending disaster or regular communication. It utilizes an auto encoder model to extract meaningful features from bird songs. Auto encoders are used to learn compact representations of data and enables efficient feature extraction by encoding bird call characteristics such as frequency, tone, and pattern variations. These extracted features are then compared with labelled data using cosine similarity. By determining the similarity between an incoming audio signal and pre-classified bird sounds, the system effectively categorizes new recordings into predefined classes. The model classifies bird vocalizations into seven distinct labels of two different species: the European Starling (Sturnus vulgaris) and the Canary (Serinus canaria). These species were chosen because they have diverse vocalizations and respond to environmental changes. This AI-driven system enables real-time monitoring and predictive analytics which significantly helps researchers, conservationists, and disaster management teams to derive actionable insights. By analyzing variations in bird calls, the system enhances early warning mechanisms for natural disasters such as earthquakes, and forest fires. This project goes beyond disaster detection and helps conserve biodiversity and study ecosystems. Understanding bird sounds can help protect bird species and keep nature balanced. It combines bioacoustics, deep learning, and environmental science to improve nature-based forecasting.