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 microphone-array 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.
Highlights
W ITH the ability of hovering above the terrain and moving in 3D, multi-rotor micro aerial vehicles (MAV) are an ideal mobile sensing platform that can be equipped with cameras, laser scanners, ultrasonic radars and microphones [1]
We addressed the problem of acoustic sensing using multiple microphones mounted on an MAV
The main challenge is dealing with extremely low signal-to-noise ratios (SNR) that degrade the sound recording quality significantly because of the ego-noise generated by motors and propellers
Summary
W ITH the ability of hovering above the terrain and moving in 3D, multi-rotor micro aerial vehicles (MAV) are an ideal mobile sensing platform that can be equipped with cameras, laser scanners, ultrasonic radars and microphones [1]. Since the motors and propellers are closer to the microphones than the target sound source, an MAV sound recording usually presents an extremely low signal-to-noise ratio (SNR), which considerably degrades the performance of most microphonearray signal processing algorithms. The spectrum of this nonstationary ego-noise depends on the rotation speed of each motor, which changes over time. To fill the gap between the extensive work in microphonearray signal processing and the new applications to MAVs, after introducing the problem formulation, we present in Section III three types of unsupervised microphonearray algorithms that can be used for ego-noise reduction: time-frequency spatial filtering (a recently emerged technique), beamforming and blind source separation.
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