Abstract-When a micro aerial vehicle (MAV) captures sounds emitted by a ground or aerial source, its motors and propellers are much closer to the microphone(s) than the sound source, thus leading to extremely low signal-to-noise ratios (SNR), e.g., −15 dB. While microphone-array techniques have been investigated intensively, their application to MAV-based ego-noise reduction has been rarely reported in the literature. To fill this gap, we implement and compare three types of microphonearray algorithms to enhance the target sound captured by an MAV. These algorithms include a recently emerged technique, time-frequency spatial filtering, and two well-known techniques, beamforming and blind source separation. In particular, based on the observation that the target sound and the ego-noise usually have concentrated energy at sparsely isolated time-frequency bins, we propose to use the time-frequency processing approach, which formulates a spatial filter that can enhance a target direction based on local direction of arrival estimates at individual time-frequency bins. By exploiting the time-frequency sparsity of the acoustic signal, this spatial filter works robustly for sound enhancement in the presence of strong ego-noise. We analyze in details the three techniques and conduct a comparative evaluation with real-recorded MAV sounds. Experimental results show the superiority of blind source separation and time-frequency filtering in low-SNR scenarios.
Time-frequency processing, a recently proposed approach, exploits the sparsity of the acoustic signal in the timefrequency domain to design a spatial filter that enhances the signal from a desired direction REF .
Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles
IEEE Sensors Journal
IEEE Sensors Journal
Engineering