Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential\nstep for on-board sound source localization and separation. This could be useful for search & rescue\noperations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the\npreviously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented,\nwhich is a set of recordings produced by the ego-noise of a drone performing different aerial\nmaneuvers and by other drones flying nearby. It was found that the recordings have a very low\nSignal-to-Noise Ratio (SNR), that the noise is dynamic depending of the droneâ??s movements, and\nthat their noise signatures are highly correlated. Three popular filtering techniques were evaluated\nin this work in terms of noise reduction and signature extraction, which are: Beroutiâ??s Non-Linear\nNoise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled\nRecursive Averaging. Although there was moderate success in noise reduction, no filter was able to\nkeep intact the signature of the drone flying in parallel. These results are evidence of the challenge in\naudio processing over drones, implying that this is a field prime for further research.
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