Automated bird Species Identification using Audio Signal Processing and Neural Network

Project Algorithm :

OpenCV, NumPy, Scikit-learn, TensorFlow

Project Overview :

Automated bird species identification plays a crucial role in ecological monitoring, biodiversity assessment, and conservation efforts. This study presents a system that leverages audio signal processing and neural networks to accurately classify bird species based on their vocalizations. The proposed approach involves preprocessing raw bird song recordings using techniques such as noise reduction, Mel-frequency cepstral coefficients (MFCC) extraction, and spectrogram analysis to capture essential acoustic features. These features are then used to train a convolutional neural network (CNN) designed to learn and distinguish between the unique audio patterns of various bird species. The model demonstrates high classification accuracy across a diverse dataset of bird calls, highlighting its potential for real-time, scalable deployment in natural environments. This work contributes to the advancement of bioacoustic monitoring by offering an efficient, non-invasive tool for automated species recognition.

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