Skip to main content
SpringerLink
Log in

Drone sound detection system based on feature result-level fusion using deep learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Drones have attracted more and more attention due to the convenience and wide applications, and they are playing important roles for both civilian and military. But these also pose threats to human life and invasion to privacy, requiring effective and low-cost detection of drones in important unattended areas. In this paper, we propose the result-level fusion convolutional neural network (CNN) network to detect drones and distinguish whether there are drones in the surrounding environment. Log-Mel spectrogram and Mel frequency cepstral coefficient (MFCC) were used to extract the features of sound signals, and input the two features into the networks separately, then fuse the results from the two networks with evidence theory to obtain the final detection result. The experimental results show that the accuracy of the drone detection based on deep learning method is higher than the machine learning method, the result-level fusion can combine the advantages of different features and increase the accuracy to 94.5%. Furthermore, the results show that the proposed drone sound detection system can achieve effective detection within 50 m.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

All date generated or analysed during this study are included in this published article (and its supplementary information files).

All authors contributed to the study conception and design. Material preparation, data collection and analysis and writing original draft were performed by Dong Qiushi. Resources, supervision, funding acquisition were performed by Liu Yu. Material preparation, software, investigation were performed by Liu Xiaolin. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

References

  1. Alhaji Musa S, Raja Abdullah RSA, Sali A, Ismail A, Abdul Rashid NE (2019) Low-slow-small (LSS) target detection based on micro Doppler analysis in forward scattering radar geometry. Sensors 19(15):3332

    Article  Google Scholar 

  2. Anwar MZ, Kaleem Z, Jamalipour A (2019) Machine learning inspired sound-based amateur drone detection for public safety applications. IEEE Trans Veh Technol 68(3):2526–2534

    Article  Google Scholar 

  3. Baek S, Jung Y, Lee S (2021) Signal expansion method in indoor FMCW radar Systems for Improving Range Resolution[J]. Sensors 21(12):4226

    Article  Google Scholar 

  4. Cerutti G, Prasad R, Brutti A, Farella E (2020) Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms. IEEE J Sel Top Signal Process 14(4):654–664

    Article  Google Scholar 

  5. Dempster AP (1966) New methods for reasoning towards posterior distributions based on sample data. Ann Math Stat 37(2):355–374

    Article  MathSciNet  MATH  Google Scholar 

  6. Dogru S, Marques L (2020) Pursuing drones with drones using millimeter wave radar. IEEE Robot Autom Lett 5(3):4156–4163

    Article  Google Scholar 

  7. Espinosa R, Ponce H, Gutiérrez S (2021) Click-event sound detection in automotive industry using machine/deep learning[J]. Appl Soft Comput 108:107465

    Article  Google Scholar 

  8. Fu H, Abeywickrama S, Zhang L, Yuen C (2018) Low-complexity portable passive drone surveillance via SDR-based signal processing. IEEE Commun Mag 56(4):112–118

    Article  Google Scholar 

  9. Guo J , Ahmad I , Chang KH (2020) Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming. EURASIP J Wirel Commun Netw 2020(1)

  10. Khan T (2019) A deep learning model for snoring detection and vibration notification using a smart wearable gadget. Electronics 8(9):987

    Article  Google Scholar 

  11. Kim J, Min K, Jung M, Chi S (2020) Occupant behavior monitoring and emergency event detection in single-person households using deep learning-based sound recognition. Build Environ 181:107092

    Article  Google Scholar 

  12. Kong Q, Xu Y, Sobieraj I, Wang W, Plumbley MD (2019) Sound event detection and time–frequency segmentation from weakly labelled data. IEEE-ACM Trans Audio Speech Lang 27(4):777–787

    Article  Google Scholar 

  13. Kong Q, Xu Y, Wang W, Plumbley MD (2020) Sound event detection of weakly labelled data with CNN-transformer and automatic threshold optimization. IEEE-ACM Trans Audio Speech Lang 28:2450–2460

    Article  Google Scholar 

  14. Meng F, Shi Y, Wang N, Cai M, Luo Z (2020) Detection of respiratory sounds based on wavelet coefficients and machine learning. IEEE Access 8:155710–155720

    Article  Google Scholar 

  15. Musa SA, Abdullah R, Sali A et al (2019) Low-slow-small (LSS) target detection based on Micro Doppler analysis in forward scattering radar geometry[J]. Sensors 19(15):3332

    Article  Google Scholar 

  16. Mushtaq Z, Su SF (2020) Environmental sound classification using a regularized deep convolutional neural network with data augmentation. Appl Acoust 167:107389

    Article  Google Scholar 

  17. Nguyen P, Truong H, Ravindranathan M, Nguyen A, Han R, Vu T (2018) Cost-effective and passive rf-based drone presence detection and characterization. Mob Comput Commun Rev 21(4):30–34

    Google Scholar 

  18. Park J, Jung DH, Bae KB, Park SO (2020) Range-Doppler map improvement in FMCW radar for small moving drone detection using the stationary point concentration technique. IEEE Trans Microw Theory Tech 68(5):1858–1871

    Article  Google Scholar 

  19. Rahman S, Robertson DA (2019) Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images. IET Radar Sonar Navig 14(5):653–661

    Article  Google Scholar 

  20. Reineking T (2014) Belief functions: theory and algorithms. Universität Bremen, Dissertation

    MATH  Google Scholar 

  21. Siddagangaiah S, Chen CF, Hu WC, Akamatsu T, McElligott M, Lammers MO, Pieretti N (2020) Automatic detection of dolphin whistles and clicks based on entropy approach. Ecol Indic 117:106559

    Article  Google Scholar 

  22. Siemiatkowska B, Stecz W (2021) A framework for planning and execution of drone swarm missions in a hostile environment[J]. Sensors 21(12):4150

    Article  Google Scholar 

  23. Su Y, Zhang K, Wang J, Madani K (2019) Environment sound classification using a two-stream CNN based on decision-level fusion. Sensors 19(7):1733

    Article  Google Scholar 

  24. Suman A, Kumar C (2020) An approach to detect the accident in VANETs using acoustic signal. Appl Acoust 163:107205

    Article  Google Scholar 

  25. Uddin Z, Altaf M, Bilal M, Nkenyereye L, Bashir AK (2020) Amateur drones detection: a machine learning approach utilizing the acoustic signals in the presence of strong interference. Comput Commun 154:236–245

    Article  Google Scholar 

  26. Vafeiadis A, Votis K, Giakoumis D, Tzovaras D, Chen L, Hamzaoui R (2020) Audio content analysis for unobtrusive event detection in smart homes. Eng Appl Artif Intell 89:103226

    Article  Google Scholar 

  27. Xia X, Togneri R, Sohel F, Zhao Y, Huang D (2019) Multi-task learning for acoustic event detection using event and frame position information. IEEE Trans Multimedia 22(3):569–578

    Article  Google Scholar 

  28. Zegart A (2020) Cheap fights, credible threats: the future of armed drones and coercion. J Strateg Stud 43(1):6–46

    Article  Google Scholar 

  29. Zhu Y, Liu L, Lu Z et al (2019) Target detection performance analysis of FDA-MIMO Radar[J]. IEEE Access PP(99):1–1

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51,875,094 and 51,775,085) and the Fundamental Research Funds for the Central Universities (Grant Nos. N2003011).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang, 110819, China

    Qiushi Dong, Yu Liu & Xiaolin Liu

Authors
  1. Qiushi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaolin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Liu.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, Q., Liu, Y. & Liu, X. Drone sound detection system based on feature result-level fusion using deep learning. Multimed Tools Appl 82, 149–171 (2023). https://doi.org/10.1007/s11042-022-12964-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12964-3

Keywords

Search

Navigation