This study provides a detailed analysis of how acoustic signals generated by drones are perceived by the human ear compared to their detection by advanced microphone-based systems. The research includes different types of drones and uses a psychoacoustic model to evaluate the perceptual sound power of these signals. Metrics within this model are utilized to determine the maximum distance at which UAV sound remains audible as a function of frequency. In addition, a detection system integrating machine learning methods and the YAMNet neural network is implemented to investigate how drone acoustic signals are affected by factors such as distance, frequency, and surface reflections during propagation. The primary aim of this study is to demonstrate that even the simplest acoustic detection systems significantly outperform human hearing in identifying drones. The results are expected to highlight the effectiveness and versatility of these systems, emphasizing their potential as key tools to enhance security and surveillance in real-world scenarios.