Identification and Classification of Singing Birds Using Machine Learning

The identification and classification of singing birds are fundamental tasks in ecological research, contributing to the understanding of avian biodiversity and behavioral patterns. In this study, we propose a novel approach that leverages machine learning techniques in conjunction with audio descriptors, including Mel-Frequency Cepstral Coefficients (MFCC), spectral characteristics, and timber-related features, to automate the categorization of singing birds. Two powerful classifiers, the Feedforward BackPropagation Neural Network (FBN) and Support Vector Machine (SVM), are employed to effectively differentiate and classify bird species based on their vocalizations. The research commences with the extraction of pertinent audio features, capturing the distinct acoustic profiles of singing birds. MFCCs provide critical information on the frequency content, while spectral and timber attributes offer insights into the tonal and temporal characteristics of the vocalizations. These audio descriptors serve as the foundation for the machine learning classifiers to distinguish between various bird species. The efficacy of this methodology is assessed using standard performance metrics, such as accuracy, precision, recall, and F1-score. The results illustrate that the integration of advanced audio descriptors and machine learning algorithms enables accurate identification and classification of singing birds. This innovative approach not only enhances ecological monitoring but also contributes to the broader knowledge of avian behavior and population dynamics. The fusion of audio descriptors and machine learning methods in the identification and classification of singing birds promises to be a valuable tool in ecological research, facilitating non-invasive assessment of bird populations and enhancing our understanding of avian ecology.