Browse > Article
http://dx.doi.org/10.13089/JKIISC.2022.32.5.881

A General Acoustic Drone Detection Using Noise Reduction Preprocessing  

Kang, Hae Young (Korea University)
Lee, Kyung-ho (Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.32, no.5, 2022 , pp. 881-890 More about this Journal
Abstract
As individual and group users actively use drones, the risks (Intrusion, Information leakage, and Sircraft crashes and so on) in no-fly zones are also increasing. Therefore, it is necessary to build a system that can detect drones intruding into the no-fly zone. General acoustic drone detection researches do not derive location-independent performance by directly learning drone sound including environmental noise in a deep learning model to overcome environmental noise. In this paper, we propose a drone detection system that collects sounds including environmental noise, and detects drones by removing noise from target sound. After removing environmental noise from the collected sound, the proposed system predicts the drone sound using Mel spectrogram and CNN deep learning. As a result, It is confirmed that the drone detection performance, which was weak due to unstudied environmental noises, can be improved by more than 7%.
Keywords
Acoustic Drone Detection; Noise Cancellation; Noise Reduction; Mel Spectrogram; Convolutional Neural Network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 H. Liu, Z. Wei, Y. Chen, J. Pan, L.Lin and Y. Ren, "Drone detectionbased on an audio-assisted cameraarray," 2017 IEEE 3rd International Conference on Multimedia BigData(BigMM), pp. 402-406, Apr. 2017.
2 A. Sedunov, D. Haddad, H. Salloum,A. Sutin, N. Sedunov and A.Yakubovskiy, "Stevens drone detectionacoustic system and experimentsinacoustics uav tracking," 2019 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1-7, Nov. 2019.
3 I. Aydin and E. Kizilay, "Development of a new light-weight convolutional neural network for acoustic-basedamateur drone detection," AppliedAcoustics, vol. 193, pp. 108773, May.2022.
4 A. Choi-Fitzpatrick, "Drones for good:technological innovations, social movements, and the state," Journal of International Affairs, vol. 68, no. 1, pp. 19-36, Oct. 2014.
5 B.Yang, E.T. Matson, A.H. Smith, J.E. Dietz and J.C. Gallagher, "Uav detection system with multiple acoustic nodes using machine learning models," 2019 3rd IEEE International Conference on Robotic Computing(IRC), pp. 493-498, Feb. 2019.
6 P. Casabianca and Y. Zhang, "Acoustic-based uav detection using late fusion of deep neural networks," Drones, vol. 5, no. 3, pp. 54. Jun. 2021.
7 Z. Shi, X. Chang, C. Yang, Z. Wuand J. Wu, "An acoustic-based surveillance system for amateur drones detection and localization," IEEE transactions on vehicular technology, vol. 69, no. 3, pp. 2731-2739, Jan. 2020.   DOI
8 I. Guvenc, F. Koohifar, S. Singh, M.L. Sichitiu and D. Matolak, "Detection, tracking, and interdiction for amateur drones," IEEE Communications Magazine, vol. 56, no. 4, pp. 75-81, Apr. 2018.   DOI
9 D. Gettinger and A.H. Michel, "Drone sightings and close encounters: an analysis," Center for the Study of the Drone, Bard College, Dec. 2015.
10 S. Park, H.T. Kim, S. Lee, H. Joo and H. Kim, "Survey on anti-drone systems: components, designs, and challenges," IEEE Access, vol. 9, pp. 42635-42659, Mar. 2021.   DOI
11 S. Al-Emadi, A. Al-Ali, A. Mohammad and A. Al-Ali, "Audio based drone detection and identification using deep learning," 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 459-464, Jun. 2019.
12 A. Bernardini, F. Mangiatordi, E. Pallotti and L. Capodiferro, "Drone detection by acoustic signature identification," Electronic Imaging, vol. 29, no.10, pp. 60-64, Jan. 2017.
13 Q. Dong, Y. Liu and X. Liu, "Dronesound detection systembasedonfeature result-level fusion usingdeeplearning," Multimedia Tools andApplications, pp. 1-23, Jun. 2022.
14 F. Christnacher, S. Hengy, M.Laurenzis, A. Matwyschuk, P. Naz, S.Schertzer and G. Schmitt, "Optical and acoustical uav detection," Electro-Optical Remote SensingX,Vol. 9988, pp. 83-95, Oct. 2016.
15 J. Busset, F. Perrodin, P. Wellig, B.Ott, K. Heutschi, T. Ruhl and T.Nussbaumer, "Detection and trackingof drones using advanced acousticcameras," Unmanned/UnattendedSensors and Sensor Networks XI; andAdvanced Free-Space Optical Communication Techniques andApplications, Vol. 9647, pp. 53-60,Oct. 2015.
16 Y. Seo, B. Jang and S. Im, "Dronedetection using convolutional neural networks with acoustic stft features," In 2018 15th IEEE International Conference on Advanced VideoandSignal Based Surveillance (AVSS),pp. 1-6, Nov. 2018.
17 S. Jeon, J.W. Shin, Y.J. Lee, W.H. Kim, Y. Kwon and H.Y. Yang, "Empirical study of drone sound detection in real-life environment with deep neural networks," 2017 25th European Signal Processing Conference (EUSIPCO), pp. 1858-1862, Aug. 2017.
18 Z. Uddin, J. Nebhen, M. Altaf andF.A. Orakzai, "Independent vectoranalysis inspired amateur dronedetection through acoustic signals," IEEE Access, vol. 9, pp. 63456-63462,Apr. 2021.   DOI
19 H. Kolamunna, T. Dahanayaka, J. Li,S. Seneviratne, K. Thilakaratne, A.Y.Zomaya and A. Seneviratne,"Droneprint: acoustic signaturesforopen-set drone detectionandidentification with online data," Proceedings of the acmon interactive,mobile, wearable and ubiquitoustechnologies, vol. 5, no. 1, pp. 1-31,Mar. 2021.
20 S. Salman, J. Mir, M.T. Farooq, A.N.Malik and R. Haleemdeen, "Machinelearning inspired efficient audio dronedetection using acoustic features," In 2021 International BhurbanConference on Applied Sciences andTechnologies (IBCAST), pp. 335-339,Jan. 2021.
21 M.Z. Anwar, Z. Kaleem and A. Jamalipour, "Machine learning inspired sound-based amateur drone detection for public safety applications," IEEE Transactions on Vehicular Technology, vol. 68, no.3, pp. 2526-2534, Jan. 2019.   DOI
22 D. Utebayeva, A. Almagambetov, M. Alduraibi, Y. Temirgaliyev, L. Ilipbayeva and S. Marxuly, "Multi-label uav sound classification using stacked bidirectional lstm," 2020 4th IEEE International Conference on Robotic Computing (IRC), pp. 453-458, Nov. 2020.
23 A. Rabaoui, M. Davy, S. Rossignol and N. Ellouze, "Using one-class SVMs and wavelets for audiosurveillance," IEEE Transactionson information forensics and security, vol. 3, no. 4, pp. 763-775, Nov. 2008.   DOI
24 The New York Times, "Two major Saudi oil installations hit by drone strike, and US blames Iran," https://www.nytimes.com/2019/09/14/world/middleeast/saudi-arabia-refineries-drone-attack.html, Sep. 2019.
25 S. Jamil, M. Rahman, A. Ullah, S.Badnava, M. Forsat andS.S.Mirjavadi, "Malicious uav detectionusing integrated audio andvisual features for public safetyapplications," Sensors, vol. 20, no. 14,pp. 3923, Jul. 2020.
26 DFS Deutsche Flugsicherung GmbH, "Drones Still a Problem Even with Little Traffic," https://www.dfs.de/homepage/en/media/press/2021/18-01-2021-drones-still-a-problem-even-with-little-traffic/, Jan. 2021.
27 J.P. Yaacoub, H. Noura, O. Salman and A. Chehab, "Security analysis of drones systems: Attacks, limitations, and recommendations," Internet of Things, vol. 11, pp. 100218, Sep. 2020.
28 M.A. Khan, H. Menouar, A. Eldeeb, A. Abu-Dayya and F.D. Salim, "On the detection of unauthorized drones-techniques and future perspectives: a review," IEEE Sensors Journal, vol. 22, no. 12, pp.11439-11455, Apr. 2022.   DOI
29 U. Seidaliyeva, D. Akhmetov, L.Ilipbayeva and E.T. Matson,"Real-time and accurate dronedetection in a video with astaticbackground," Sensors, vol. 20, no. 14,pp. 3856, Jul. 2020.
30 X. Shi, C. Yang, W. Xie, C. Liang, Z.Shi and J. Chen, "Anti-drone systemwith multiple surveillancetechnologies: architecture,implementation, and challenges," IEEE Communications Magazine, vol.56, no. 4, pp. 68-74, Apr. 2018.   DOI
31 M. Nijim and N. Mantrawadi, "Drone classification and identification system by phenome analysis using data mining techniques," In 2016 IEEE Symposium on Technologies for Homeland Security (HST), pp. 1-5, May. 2016.