Tom Hirshberg, M.Sc. Thesis Seminar
Advisor: Prof. Alex M. Bronstein
To model the self-sound of drones, acoustics analysis and high-fidelity computational fluid dynamic methods can be used. However, these methods require significant computational resources. Therefore, data-driven and analytical methods are commonly used to model the sound source, enabling the generation of a pressure-time history of the moving rotors along a time varying shaft position. We suggest a simple and low computational data-driven method for modeling the sound source of a drone in free space and indoors. Our model relies on a new high quality ground truth dataset of a spinning rotor in free space that we share publicly. Aiming beyond the sound source modeling, the model simplicity allows solving inverse problems such as indoor self-localization. As a proof of concept, we show how to improve the localization accuracy based on simple phase modulations.