Print Email Facebook Twitter Acoustic-Based Aircraft Detection and Ego-Noise Suppression Title Acoustic-Based Aircraft Detection and Ego-Noise Suppression: for Micro Aerial Vehicles Author van der Woude, Mark (TU Delft Aerospace Engineering) Contributor van Dijk, Tom (mentor) de Croon, G.C.H.E. (mentor) Snellen, M. (graduation committee) de Wagter, C. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2021-03-04 Abstract Widespread usage of Micro Aerial Vehicles (MAVs) has led to various airspace safety breaches, including near mid-air collisions with other aircraft. To ensure safe integration into general aviation, it is paramount that MAVs are equipped with an autonomous detect and avoid system when flying beyond the visual line-of-sight of the operator. The purpose of this research is to investigate the feasibility of acoustic-based aircraft detection, which has generally been overlooked in favor of optical or radar-based technology. Effective sound-based aircraft detection on-board an MAV requires suppressing the dynamic ego-noise it generates during flight, which would otherwise pollute the recorded environmental sound. This paper proposes using a recurrent neural network to predict the generated noise, given a sequence of MAV flight data, so that it can be effectively removed from noisy recordings. For aircraft detection, a convolutional neural network in combination with Mel spectrogram features is designed to classify noise-free environmental sound as either aircraft or non-aircraft, achieving 97.5\% accuracy. To reconstruct the noisy environment of an MAV flight, these noise-free sounds are mixed with ego-noise at mix ratios up to 1.00. When evaluating in these mismatched conditions, accuracy decreases to 95.0\% and 47.5\% with- and without ego-noise suppression, respectively. Although ego-noise suppression can not prevent a drop in performance, the large difference between the mismatched conditions does demonstrate the benefits of the proposed denoising approach on aircraft detection. Subject Aircraft DetectionSound Event ClassificationEgo-Noise SuppressionMel SpectrogramArtificial Neural Networks To reference this document use: http://resolver.tudelft.nl/uuid:9f478516-7129-4e6f-9b84-d66fad419d51 Embargo date 2021-03-04 Part of collection Student theses Document type master thesis Rights © 2021 Mark van der Woude Files PDF Thesis_MarkvanderWoude_ai ... ection.pdf 4.51 MB Close viewer /islandora/object/uuid:9f478516-7129-4e6f-9b84-d66fad419d51/datastream/OBJ/view