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Conference Papers Year : 2018

DREGON: Dataset and Methods for UAV-Embedded Sound Source Localization


This paper introduces DREGON, a novel publicly-available dataset that aims at pushing research in sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV). The dataset contains both clean and noisy in-flight audio recordings continuously annotated with the 3D position of the target sound source using an accurate motion capture system. In addition, various signals of interests are available such as the rotational speed of individual rotors and inertial measurements at all time. Besides introducing the dataset, this paper sheds light on the specific properties, challenges and opportunities brought by the emerging task of UAV-embedded sound source localization. Several baseline methods are evaluated and compared on the dataset, with real-time applicability in mind. Very promising results are obtained for the localization of a broad-band source in loud noise conditions, while speech localization remains a challenge under extreme noise levels.
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hal-01854878 , version 1 (07-08-2018)



Martin Strauss, Pol Mordel, Victor Miguet, Antoine Deleforge. DREGON: Dataset and Methods for UAV-Embedded Sound Source Localization. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Oct 2018, Madrid, Spain. pp.5735-5742, ⟨10.1109/IROS.2018.8593581⟩. ⟨hal-01854878⟩
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