Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Alhilal MS, Soudani A, Al-Dhelaan A (2015) Image-based object identification for efficient event-driven sensing in wireless multimedia sensor networks. Int J Distrib Sens Netw 2015:24

    Google Scholar 

  2. Bhatt R, Datta R (2016) A two-tier strategy for priority based critical event surveillance with wireless multimedia sensors. Wirel Netw 22(1):267–284

    Article  Google Scholar 

  3. Bilinska K, Filo M, Krystowski R (2007) Mica, Mica2, MicaZ. [Online]. Available: wwwpub.zih.tu-dresden.de/∼dargie/wsn/slides/students/MICA.ppt

  4. Cai Y, Lou W, Li M, Li X-Y (2009) Energy efficient target-oriented scheduling in directional sensor networks. IEEE Trans Comput 58(9):1259–1274

    Article  MathSciNet  MATH  Google Scholar 

  5. Ghadi M, Laouamer L, Moulahi T (2016) Securing data exchange in wireless multimedia sensor networks: perspectives and challenges. Multimedia Tools Appl 75(6):3425–3451

    Article  Google Scholar 

  6. Huang C-J, Yang Y-J, Yang D-X, Chen Y-J (2009) Frog classification using machine learning techniques. Expert Syst Appl 36(2):3737–3743

    Article  Google Scholar 

  7. Kiktova-Vozarikova E, Juhar J, Cizmar A (2015) Feature selection for acoustic events detection. Multimedia Tools Appl 74(12):4213–4233

    Article  Google Scholar 

  8. Kos M, Kačič Z, Vlaj D (2013) Acoustic classification and segmentation using modified spectral roll-off and variance-based features. Digital Signal Process 23(2):659–674

    Article  MathSciNet  Google Scholar 

  9. Kotus J, Lopatka K, Czyzewski A (2014) Detection and localization of selected acoustic events in acoustic field for smart surveillance applications. Multimedia Tools Appl 68(1):5–21

    Article  Google Scholar 

  10. Küçükbay SE, Sert M (2015a) Audio-based event detection in office live environments using optimized MFCC-SVM approach. InSemantic Computing (ICSC), 2015 I.E. International Conference on, pp. 475–480 IEEE

  11. Küçükbay SE, Sert M (2015b) Audio-based event detection in office live environments using optimized MFCC-SVM approach. In Semantic Computing (ICSC), 2015 I.E. International Conference on, pp. 475–480 IEEE

  12. Lee Y, Han DK, Ko H (2013) Acoustic signal based abnormal event detection in indoor environment using multiclass adaboost. IEEE Trans Consum Electron 59(3):615–622

    Article  Google Scholar 

  13. Li Q, Liu X, Yang X, Li T (2015) Abnormal event detection method in multimedia sensor networks. Int J Distrib Sens Netw 2015:4

    Google Scholar 

  14. Lopatka K, Kotus J, Czyzewski A (2015) Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations. Multimedia Tools Appl 75:1–33

    Google Scholar 

  15. Ludeña-Choez J, Gallardo-Antolín A (2015) Feature extraction based on the high-pass filtering of audio signals for acoustic event classification. Comput Speech Lang 30(1):32–42

    Article  Google Scholar 

  16. Mellinger DK, Martin SW, Morrissey RP, Thomas L, Yosco JJ (2011) A method for detecting whistles, moans, and other frequency contour sounds. J Acoust Soc Am 129(6):4055–4061

    Article  Google Scholar 

  17. Mesaros A, Heittola T, Eronen A, Virtanen T (2010) Acoustic event detection in real life recordings. In Signal Processing Conference, 2010 18th European, pp. 1267–1271 IEEE

  18. Molina-Pico A, Cuesta-Frau D, Araujo A, Alejandre J, Rozas A (2016) Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network. J Sens 2016

  19. Peng G, Shi X, Kadowaki T (2015) Evolution of TRP channels inferred by their classification in diverse animal species. Mol Phylogenet Evol 84:145–157

    Article  Google Scholar 

  20. Phan H, Maaß M, Mazur R, Mertins A (2015) Random regression forests for acoustic event detection and classification. IEEE/ACM Trans Audio Speech Lang Process 23(1):20–31

    Article  Google Scholar 

  21. Sahin YG, Ince T (2009) Early forest fire detection using radio-acoustic sounding system. Sens 9(3):1485–1498

    Article  Google Scholar 

  22. Sandhan T, Sonowal S, Choi JY (2014) Audio bank: A high-level acoustic signal representation for audio event recognition. In Control, Automation and Systems (ICCAS), 2014 14th International Conference on, pp. 82–87 IEEE

  23. Singh VK, Kumar M (2016) Hierarchical compressed sensing for cluster based wireless sensor networks. Int J Adv Comput Sci Appl 1(7):58–67

    Google Scholar 

  24. Talukder A, Panangadan A (2014) Extreme event detection and assimilation from multimedia sources. Multimedia Tools Appl 70(1):237–261

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Vishal Krishna Singh, Gajendra Sharma & Manish Kumar

Authors
  1. Vishal Krishna Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gajendra Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vishal Krishna Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, V.K., Sharma, G. & Kumar, M. Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors. Multimed Tools Appl 76, 18531–18555 (2017). https://doi.org/10.1007/s11042-016-4241-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4241-1

Keywords

Search

Navigation