Deep learning-based bird species classification using acoustic features: A convolutional neural network approach

Identification of bird species via vocalisations is important for biodiversity research, conservation initiatives, and ecological monitoring. This study proposes a deep learning-based method for categorising different species of birds by employing sound characteristics taken from their vocalisations. Utilising a Convolutional Neural Network (CNN) model, we examine attributes obtained from the audio recordings, including Spectral Roll-Off, Constant-Q Transform (CQT), Chromatogram, and Spectrogram. The audio information are input into the CNN to enable precise classification, and the dataset includes several bird species. Plotting a confusion matrix also helps to provide light on how well the model performs for various bird classes. This study demonstrates how deep learning methods may improve the identification of bird species using audio analysis, offering a useful instrument for ecological and conservation studies.