BIRD SPECIES IDENTIFICATION

The identification of bird species through their vocalizations has significant implications for biodiversity monitoring, ecological research, and conservation efforts. Traditional methods, which rely on expert knowledge and manual analysis, are time-consuming and often impractical at scale. This study presents an automated system for bird species identification using audio signal processing techniques combined with machine learning algorithms. The proposed approach involves preprocessing raw bird sound recordings to extract meaningful audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), chroma features, and spectral contrast. These features are then used to train and evaluate various machine learning classifiers, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNNs). Experimental results demonstrate that the system can accurately classify multiple bird species across a diverse dataset, achieving promising levels of accuracy and generalizability. This research highlights the potential of integrating signal processing and machine learning to develop scalable, real-time solutions for avian acoustic monitoring in natural environments