Aerial drones (or flying robots) based inspection monitoring is fast turning into an high value asset for applications across sectors. Our focus in this work is to develop the "Ear" or the auditory sensing system of the drone so as to advance it sensing abilities beyond vision (the default external sensor) for it to better serve a wide range of inspection scenarios. However, devising this capability is extremely challenging due to severe acoustic self-interference from its rotor blades. In this work, we investigate the related challenges and overcome some of the initial hurdles by proposing a subspace based denoising algorithm, followed by its application in electricity power distribution systems.