<p>The growing popularity and increased accessibility of unmanned aerial vehicles (UAVs) have raised concerns about potential threats they may pose. In response, researchers have devoted significant efforts to developing UAV detection and classification systems, utilizing diverse methodologies such as computer vision, radar, radio frequency, and audio-based approaches. However, the availability of publicly accessible UAV audio datasets remains limited. Consequently, this research endeavor was undertaken to address this gap by undertaking the collection of a comprehensive UAV audio dataset, alongside the development of a precise and efficient audio-based UAV classification system.</p> <p>This research project is structured into three distinct phases, each serving a unique purpose in data collection and training the proposed UAV classifier. These phases encompass data collection, dataset evaluation, the implementation of a proposed convolutional neural network, training procedures, as well as an in-depth analysis and evaluation of the obtained results. To assess the effectiveness of the model, several evaluation metrics are employed, including training accuracy, loss rate, the confusion matrix, and ROC curves.</p> <p>The findings from this study conclusively demonstrate that the proposed CNN classi- fier exhibits nearly flawless performance in accurately classifying UAVs across 22 distinct categories.</p>