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Article

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

1
Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
2
KINDI Center for Computing Research, College of Engineering, Qatar University, Doha 2713, Qatar
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in “Sara Al-Emadi, Abdulla Al-Ali, Amr Mohammad, and Abdulaziz Al-Ali. Audio-Based Drone Detection and Identification using Deep Learning. In Proceedings of the 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, 24 June 2019”.
Academic Editors: Dimitrios Zarpalas, Anastasios Dimou, Angelo Coluccia, Alessio Fascista, Arne Schumann and Lars Sommer
Sensors 2021, 21(15), 4953; https://doi.org/10.3390/s21154953
Received: 19 May 2021 / Revised: 14 July 2021 / Accepted: 15 July 2021 / Published: 21 July 2021
(This article belongs to the Special Issue Deep Learning Based UAV Detection, Classification, and Tracking)
Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones. View Full-Text
Keywords: drone; UAV; machine learning; deep learning; Convolutional Neural Network CNN; Recurrent Neural Network RNN; Convolutional Recurrent Neural Network CRNN; Generative Adversarial Networks GAN; acoustic fingerprinting; drone audio dataset; artificial intelligence; drone detection; drone identification drone; UAV; machine learning; deep learning; Convolutional Neural Network CNN; Recurrent Neural Network RNN; Convolutional Recurrent Neural Network CRNN; Generative Adversarial Networks GAN; acoustic fingerprinting; drone audio dataset; artificial intelligence; drone detection; drone identification
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MDPI and ACS Style

Al-Emadi, S.; Al-Ali, A.; Al-Ali, A. Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks. Sensors 2021, 21, 4953. https://doi.org/10.3390/s21154953

AMA Style

Al-Emadi S, Al-Ali A, Al-Ali A. Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks. Sensors. 2021; 21(15):4953. https://doi.org/10.3390/s21154953

Chicago/Turabian Style

Al-Emadi, Sara, Abdulla Al-Ali, and Abdulaziz Al-Ali. 2021. "Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks" Sensors 21, no. 15: 4953. https://doi.org/10.3390/s21154953

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