Unmanned aerial vehicles, specifically quadrotor drones, are increasingly commonplace in community and workplace settings and are often used for photography, cinematography, and small parcel transport. The presence of these flying robotic systems has a substantial impact on the surrounding environment. To better understand the ergonomic impacts of quadrotor drones, a quantitative description of their acoustic signature is needed. While previous efforts have presented detailed acoustic characterizations, there is a distinct lack of high spatial-fidelity investigations of the acoustic field of a quadrotor hovering under its own power. This work presents an experimental quantification of the spatial acoustic pressure distribution in the near-field of a live hovering unmanned aerial vehicle. A large-aperture scanning microphone array was constructed to measure sound pressure level at a total of 1728 points over a 2 m × 3 m × 1.5 m volume. A physics-infused machine learning model was fit to the data to better visualize and understand the experimental results. The experimental data and modeling presented in this work are intended to inform future design of experiments for quadrotor drone acoustics, provide quantitative information on the acoustic near-field signature, and demonstrate the utility of optical motion tracking coupled with a custom microphone array for characterization of live acoustic sources.