Print Email Facebook Twitter Hear-and-avoid for unmanned air vehicles using convolutional neural networks Title Hear-and-avoid for unmanned air vehicles using convolutional neural networks Author Wijnker, D.C. (Student TU Delft) van Dijk, Tom (TU Delft Control & Simulation) Snellen, M. (TU Delft Aircraft Noise and Climate Effects) de Croon, G.C.H.E. (TU Delft Control & Simulation) de Wagter, C. (TU Delft Control & Simulation) Date 2021 Abstract To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access Subject Unmanned air vehiclecollision avoidancehear and avoidconvolutional neural networkaircraft detectionOA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:5a62c555-af99-4d55-a695-fa3044e2e37c DOI https://doi.org/10.1177/1756829321992137 ISSN 1756-8293 Source International Journal of Micro Air Vehicles, 13 Part of collection Institutional Repository Document type journal article Rights © 2021 D.C. Wijnker, Tom van Dijk, M. Snellen, G.C.H.E. de Croon, C. de Wagter Files PDF 1756829321992137.pdf 1.26 MB Close viewer /islandora/object/uuid:5a62c555-af99-4d55-a695-fa3044e2e37c/datastream/OBJ/view