Exploring Audio Features for Enhanced Bird Identification Using Neural Networks

Machine Learning

The identification of bird species using sound signals is crucial for wildlife conservation and environmental monitoring. In this study, we present an automatic bird species recognition system that leverages Mel-frequency cepstral coefficients (MFCCs) as features and employs an Artificial Neural Network (ANN) for classification. By utilizing a dataset of bird songs, the system pre-processes audio data to remove noise and extracts important acoustic features. The proposed ANN model demonstrates strong performance in species classification, handling real-world bioacoustics challenges such as noisy environments. This paper discusses the system architecture, including audio pre-processing, feature extraction, classification, and user interface implementation, along with the results achieved through our experiments. The system's ability to generalize well in practical settings suggests its utility in large-scale bioacoustics monitoring.

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