Time-frequency processing for sound source localization from a micro aerial vehicle
We address the problem of sound source localization with a microphone array mounted on a micro aerial vehicle (MAV). Due to the noise generated by motors and propellers, this scenario is characterized by extremely low signal-to-noise ratios (SNR). Based on the observation that the energy of MAV sound recordings is usually concentrated at isolated time-frequency bins, we propose a time-frequency processing framework to address this problem. We first estimate the direction of arrival of the sound at individual time-frequency bins. Then we formulate a set of spatially informed filters pointing at candidate directions in the search space. The output of the filtering tends to present high non-Gaussianity when the spatial filter is steered towards the target sound source. Finally, by measuring the non-Gaussianity of the spatial filtering outputs we build a spatial likelihood function from which we estimate the direction of the target sound. Experimental results with real-recorded MAV ego-noise show the superiority of the proposed method over the state of the art in performing source localization robustly.