BirdClassifier: An Advanced Bird Classification Model Using Deep Neural Networks

Automated bird species classification plays a crucial role in biodiversity conservation and ecological monitoring. This study proposes a hybrid deep learning approach that integrates both acoustic and visual data to enhance classification accuracy. Bird calls are processed into Mel-spectrogram representations and classified using a Convolutional Neural Network (CNN) based on EfficientNetV2BO. Simultaneously, a CNN model is employed for species identification from image data, leveraging data augmentation techniques to improve generalization. The audio classification model benefits from transfer learning to refine feature extraction. Experimental results demonstrate that the multimodal fusion of audio and image data significantly outperforms single-modal approaches, achieving superior classification performance. The proposed system provides a scalable and robust framework for automated bird identification, with promising applications in wildlife monitoring, ecological assessments, and conservation efforts.