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This study presents an automated Bird Audio Classification system using deep learning techniques, specifically Convolutional Neural Networks (CNN) and Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction. The system demonstrates high accuracy in species classification, achieving 94% training accuracy and 91% validation accuracy, making it a valuable tool for biodiversity monitoring. Future enhancements include real-time deployment and the use of transformer-based models.

Author

gonasingh5

Language

EN