Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles

Abstract

We investigate the spectral and spatial characteristics of the ego-noise of a multirotor micro aerial vehicle (MAV) using audio signals captured with multiple onboard microphones and derive a noise model that grounds the feasibility of microphone-array techniques for noise reduction. The spectral analysis suggests that the ego-noise consists of narrowband harmonic noise and broadband noise, whose spectra vary dynamically with the motor rotation speed. The spatial analysis suggests that the ego-noise of a P-rotor MAV can be modeled as P directional noises plus one diffuse noise. Moreover, because of the fixed positions of the microphones and motors, we can assume that the acoustic mixing network of the ego-noise is stationary. We validate the proposed noise model and the stationary mixing assumption by applying blind source separation to multi-channel recordings from both a static and a moving MAV and quantify the signal-to-noise ratio improvement. Moreover, we make all the audio recordings publicly available.