Evaluation of Sound Event Detection, Classification and Localization in the Presence of Background Noise for Acoustic Surveillance of Hazardous Situations

  • Kuba Łopatka
  • Józef Kotus
  • Andrzej Czyżewski
Part of the Communications in Computer and Information Science book series (CCIS, volume 429)


Evaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for separating foreground events from the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier are introduced. The sound source localization algorithm based on the analysis of multichannel signals from the Acoustic Vector Sensor is presented. The methods are evaluated in an experiment conducted in the anechoic chamber, in which the representative events are played together with noise of differing intensity. The results of detection, classification and localization accuracy with respect to the Signal to Noise Ratio are discussed. The algorithms presented are part of an audio-visual surveillance system.


sound detection sound source localization audio surveillance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ntalampiras, S., Potamtis, I., Fakotakis, N.: An adaptive framework for acoustic monitoring of potential hazards. EURASIP J. Audio Speech Music Process. 2009, 594103, 1–15 (2009)CrossRefGoogle Scholar
  2. 2.
    Valenzise, G., Gerosa, L., Tagliasacchi, M., Antonacci, F., Sarti, A.: Scream and gunshot detection and localization for audio-surveillance systems. In: Proc. IEEE Conf. on Advanced Video and Signal Based Surveillance, London, pp. 21–26 (2007)Google Scholar
  3. 3.
    Zhuang, X., Zhou, X., Hasegawa-Johnson, M., Huang, T.: Real-world acoustic event detection. Pattern Recognition Letters 31, 1543–1551 (2010)CrossRefGoogle Scholar
  4. 4.
    Lu, L., Zhang, H., Jiang, H.: Content analysis for audio classification and segmentation. IEEE Trans. Speech Audio Process. 10(7), 504–516 (2002)CrossRefGoogle Scholar
  5. 5.
    Rabaoui, A., Kadri, H., Lachiri, Z., Ellouze, N.: Using robust features with multi-class SVMs to classify noisy sounds. In: 3rd Int. Symp. on Communications, Control and Sig. Process., Malta, pp. 594–599 (2008)Google Scholar
  6. 6.
    Dat, T., Li, H.: Sound event recognition with probabilistic distance SVMs. IEEE Trans. Audio Speech Language Process. 19(6), 1556–1568 (2010)Google Scholar
  7. 7.
    Temko, A., Nadeu, C.: Acoustic event detection in meeting room environments. Pattern Recogn. Lett. 30, 1281–1288 (2009)CrossRefGoogle Scholar
  8. 8.
    Cowling, M., Sitte, R.: Comparison of techniques for environmental sound recognition. Pattern Recogn. Lett. 24, 2895–2907 (2003)CrossRefGoogle Scholar
  9. 9.
    Raytheon BBN Technologies, “Boomerang”,
  10. 10.
    SST Inc., “ShotSpotter”,
  11. 11.
    Safety Dynamics Systems, “SENTRI”,
  12. 12.
    Kotus, J., Łopatka, K., Kopaczewski, K., Czyżewski, A.: Automatic audio-visual threat detection. In: IEEE Int. Conf. on Multimedia Communications, Services and Security (MCSS 2010), Krakow, pp. 140–144 (2010)Google Scholar
  13. 13.
    Kotus, J., Łopatka, K., Cżyzewski, A.: Detection and localization of selected acoustic events in 3D acoustic field for smart surveillance applications. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 55–63. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Hawkes, M., Nehorai, A.: Wideband source localization using a distributed acoustic vector-sensor array. IEEE Trans. Sig. Process. 51, 1479–1491 (2003)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Yoo, I., Yook, D.: Robust voice activity detection using the spectral peaks of vowel sounds. J. of the Electronics and Telecommunication Research Institute 31, 451–453 (2009)Google Scholar
  16. 16.
    Hearst, M.A.: Support vector machines. IEEE Intelligent Systems & Their Applications 13(4), 18–28 (1998)CrossRefGoogle Scholar
  17. 17.
    Rabaoui, A., Davy, M., Rossignol, S., Ellouze, N.: Using one-class SVMs and wavelets for audio surveillance. IEEE Trans. on Information Forensics and Security 3(4), 763–775 (2008)CrossRefGoogle Scholar
  18. 18.
    Łopatka, K., Żwan, P., Czyżewski, A.: Dangerous sound event recognition using support vector machine classifiers. Advances in Intelligent and Soft Computing 80, 49–57 (2010)CrossRefGoogle Scholar
  19. 19.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology (TIST) 2(3), article 27 (2011)Google Scholar
  20. 20.
    Kim, H.-G., Moreau, N., Sikora, T.: Audio classification based on MPEG-7 spectral basis representations. IEEE Trans. on Circuits and Systems for Video Technology 14(5), 716–725 (2004)CrossRefGoogle Scholar
  21. 21.
    Peeters, G.: A large set of audio features for sound description (similarity and classification) in the CUIDADO project (2004),
  22. 22.
    Żwan, P., Czyżewski, A.: Verification of the parameterization methods in the context of automatic recognition of sounds related to danger. J. of Digital Forensic Practice 3(1), 33–45 (2010)CrossRefGoogle Scholar
  23. 23.
    Machine Learning Group at University of Waikato, “Waikato Environment for Knowledge Analysis” (2012),
  24. 24.
    Platt, J.C.: Sequential minimal optimization: A fast algorithm for training support vector machines. Adv. in Kernel Methods, Support Vector Learning 208(14), 1–21 (1998)Google Scholar
  25. 25.
    Jacobsen, F., de Bree, H.E.: A comparison of two different sound intensity measurement principles. Journal of the Acoustical Society of America 118(3), 1510–1517 (2005)CrossRefGoogle Scholar
  26. 26.
    Tijs, E., de Bree, H.-E., Steltenpool, S.: Scan & Paint: a novel sound visualization technique. In: Inter-Noise 2010, Lisbon (2010)Google Scholar
  27. 27.
    Basten, T., de Bree, H.-E., Tijs, E.: Localization and tracking of aircraft with ground based 3D sound probes. In: 33rd European Rotorcraft Forum, Kazan (2007)Google Scholar
  28. 28.
    Kotus, J.: Application of passive acoustic radar to automatic localization, tracking and classification of sound sources. Information Technologies 18, 111–116 (2010)Google Scholar
  29. 29.
    Ntalampiras, S., Potamtis, I., Fakotakis, N.: Probabilistic novelty detection for acoustic surveillance under real-world conditions. IEEE Trans. on Multimedia 13(4), 713–719 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kuba Łopatka
    • 1
  • Józef Kotus
    • 1
  • Andrzej Czyżewski
    • 1
  1. 1.Faculty of Electronics,Telecommunications and Informatics, Multimedia Systems DepartmentGdańsk University of TechnologyGdańskPoland

Personalised recommendations