Tracking a moving sound source from a multi-rotor drone
Lin Wang, Ricardo Sánchez-Matilla, Andrea Cavallaro · 2018
We propose a method to track from a multi-rotor drone a moving source, such as a human speaker or an emergency whistle, whose sound is mixed with the strong ego-noise generated by rotating motors and propellers. The proposed method is independent of the specific drone and does not need pre-training nor reference signals. We first employ a time-frequency spatial filter to estimate, on short audio segments, the direction of arrival of the moving source and then we track these noisy estimations with a particle filter. We quantitatively evaluate the results using a ground-truth trajectory of the sound source obtained with an on-board camera and compare the performance of the proposed method with baseline solutions.