Abstract
Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the previously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented, which is a set of recordings produced by the ego-noise of a drone performing different aerial maneuvers and by other drones flying nearby. It was found that the recordings have a very low Signal-to-Noise Ratio (SNR), that the noise is dynamic depending of the drone’s movements, and that their noise signatures are highly correlated. Three popular filtering techniques were evaluated in this work in terms of noise reduction and signature extraction, which are: Berouti’s Non-Linear Noise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled Recursive Averaging. Although there was moderate success in noise reduction, no filter was able to keep intact the signature of the drone flying in parallel. These results are evidence of the challenge in audio processing over drones, implying that this is a field prime for further research.
Highlights
Current advances in technology have greatly improved different sectors of the industry and society.One such sector has been the area of aerial vehicles piloted by radio control
We previously introduced the Acoustic Interactions for Robot Audition (AIRA)-Unmanned Aerial Systems (UAS) corpus in [12], and we believe that it is perfectly compatible with these objectives
This paper is organized as follows: Section 2 describes the relevant parts of the AIRA-UAS corpus for completeness sake; Section 3 provides an analysis of the audio recordings in AIRA-UAS from the time-frequency domain and describe the challenges put forward by carrying out audio processing over a flying drone; Section 4 details the filtering techniques to be evaluated; Section 5 describes the evaluation methodology based on the AIRA-UAS recordings, as well as presents the results obtained from such evaluation; and Section 6 offers the work’s conclusions and future work, as well as some ideas for the UAS community
Summary
Current advances in technology have greatly improved different sectors of the industry and society. One of the major characteristics of an UAS is the sound its rotors produce while flying, which could be used for its detection and tracking in such restricted zones [8,9] Another scenario of interest is that of carrying out audio signal processing over a drone could be useful in rescue situations [10,11]. The high amount of energy of the ego-noise of the drone while flying could be considered a challenge [6] but this has not been studied extensively This is the main objective of the work presented here: to evaluate if current popular techniques are able to properly filter out the ego-noise of the drone’s motors. This paper is organized as follows: Section 2 describes the relevant parts of the AIRA-UAS corpus for completeness sake; Section 3 provides an analysis of the audio recordings in AIRA-UAS from the time-frequency domain and describe the challenges put forward by carrying out audio processing over a flying drone; Section 4 details the filtering techniques to be evaluated; Section 5 describes the evaluation methodology based on the AIRA-UAS recordings, as well as presents the results obtained from such evaluation; and Section 6 offers the work’s conclusions and future work, as well as some ideas for the UAS community
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