Multi-modal Bird Species Identification: Integrating Deep Neural Networks, Transfer Learning, and Spectrogram Analysis

The proposed research presents a comprehensive study on the identification of bird species using a multimodal approach. We explore three distinct methodologies: 1. Deep neural network based identification, 2. Spectrogram based bird audio analysis and 3. Transfer learning based feature extraction. We observed that the ResNet50 model achieved the highest accuracy of 99.40% on the test dataset, outperforming other models. This research not only provides insights into the effectiveness of deep neural networks but also explores the potential of transfer learning and spectrogram analysis for robust bird species identification across multiple modalities. This multimodal approach contributes valuable insights to the fields of ornithology, biodiversity monitoring, and wildlife conservation.