Deep and Machine Learning-Based Detection of European Bee-Eaters Using Bird Sounds

Bird sound classification plays an important role in biodiversity monitoring, species identification, and ecological observation. This study focuses on detecting European Bee-Eater vocalizations from bird audio recordings using deep learning and machine learning techniques. The BirdCLEF 2024 Additional MP3 dataset from Kaggle is used as the primary audio source for this work. The collected MP3 files are preprocessed through noise reduction, audio normalization, segmentation, and conversion into spectrogram or Mel-spectrogram representations. These transformed audio features help the models understand time-frequency patterns present in bird vocal signals. Three models are used for classification: Avian-MixNet, TFANet, and EfficientNetB3. Avian-MixNet is designed to learn multi-scale acoustic features from bird calls, while TFANet applies time-frequency attention to focus on the most informative regions of the spectrogram. EfficientNetB3 is used as an efficient convolutional neural network model for extracting deep visual features from spectrogram images. The proposed system aims to improve the accuracy and reliability of European Bee-Eater sound detection by combining audio preprocessing, feature extraction, and deep classification models. This approach can support researchers and environmental monitoring teams in identifying bird species from large audio collections with reduced manual effort. Overall, the study presents an automated and intelligent framework for bird sound-based European Bee-Eater detection.

Keywords: European Bee-Eater Detection, Bird Sound Classification, Deep Learning, Machine Learning, BirdCLEF 2024, Audio Signal Processing, Mel-Spectrogram, Avian-MixNet, TFANet, EfficientNetB3, Time-Frequency Attention, Species Identification.