Skip to main content
Top

2021 | OriginalPaper | Chapter

Audio Surveillance: Detection of Audio-Based Emergency Situations

Authors : Zhandos Dosbayev, Rustam Abdrakhmanov, Oxana Akhmetova, Marat Nurtas, Zhalgasbek Iztayev, Lyazzat Zhaidakbaeva, Lazzat Shaimerdenova

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The subject of the study was the recognition of sounds of critical situations in the audio signal. The term “critical situation” is understood as an event, the characteristic sound signs of which can speak about acoustic artifacts as a shot, a scream, a glass crash, an explosion, a siren, etc.. The paper considers the scope of audio analytics, its advantages, the history of spectral analysis, as well as analyzes and selects tools for further development of system components. In the paper, we propose our dataset that consists of 14 classes that contains 1000 sounds of each, and a model to detect emergency situations using audio processing and analytics.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Tharwat, A., Mahdi, H., Elhoseny, M., Hassanien, A.E.: Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Exp. Syst. Appl. 107, 32–44 (2018)CrossRef Tharwat, A., Mahdi, H., Elhoseny, M., Hassanien, A.E.: Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Exp. Syst. Appl. 107, 32–44 (2018)CrossRef
2.
go back to reference Vanus, J., et al.: Monitoring of the daily living activities in smart home care. Hum. Centr. Comput. Inf. Sci. 7(1), 30 (2017)CrossRef Vanus, J., et al.: Monitoring of the daily living activities in smart home care. Hum. Centr. Comput. Inf. Sci. 7(1), 30 (2017)CrossRef
4.
go back to reference Leo, M., Medioni, G., Trivedi, M., Kanade, T., Farinella, G.M.: Computer vision for assistive technologies. Comput. Vis. Image Understand. 154, 1–15 (2017)CrossRef Leo, M., Medioni, G., Trivedi, M., Kanade, T., Farinella, G.M.: Computer vision for assistive technologies. Comput. Vis. Image Understand. 154, 1–15 (2017)CrossRef
5.
go back to reference Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., Baik, S.W.: Efficient deep CNN-based fire detection and localization in video surveillance applications. IEEE Trans. Syst. Man Cybernet. Syst. 49(7), 1419–1434 (2018)CrossRef Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., Baik, S.W.: Efficient deep CNN-based fire detection and localization in video surveillance applications. IEEE Trans. Syst. Man Cybernet. Syst. 49(7), 1419–1434 (2018)CrossRef
6.
go back to reference Goldenberg, A., et al.: Use of ShotSpotter detection technology decreases prehospital time for patients sustaining gunshot wounds. J. Trauma Acute Care Surg. 87(6), 1253–1259 (2019)CrossRef Goldenberg, A., et al.: Use of ShotSpotter detection technology decreases prehospital time for patients sustaining gunshot wounds. J. Trauma Acute Care Surg. 87(6), 1253–1259 (2019)CrossRef
7.
go back to reference Weiss, A., Halevi, O., Manus, H., Springer, D.: U.S. Patent No. 10,021,457. U.S. Patent and Trademark Office, Washington, DC (2018) Weiss, A., Halevi, O., Manus, H., Springer, D.: U.S. Patent No. 10,021,457. U.S. Patent and Trademark Office, Washington, DC (2018)
9.
go back to reference Virtanen, T., Plumbley, M.D., Ellis, D. (eds.): Computational analysis of sound scenes and events, pp. 3–12. Springer, Berlin (2018)CrossRef Virtanen, T., Plumbley, M.D., Ellis, D. (eds.): Computational analysis of sound scenes and events, pp. 3–12. Springer, Berlin (2018)CrossRef
10.
go back to reference Gabriel, D., Kojima, R., Hoshiba, K., Itoyama, K., Nishida, K., Nakadai, K.: 2D sound source position estimation using microphone arrays and its application to a VR-based bird song analysis system. Adv. Robot. 33(7–8), 403–414 (2019)CrossRef Gabriel, D., Kojima, R., Hoshiba, K., Itoyama, K., Nishida, K., Nakadai, K.: 2D sound source position estimation using microphone arrays and its application to a VR-based bird song analysis system. Adv. Robot. 33(7–8), 403–414 (2019)CrossRef
11.
go back to reference Morehead, A., Ogden, L., Magee, G., Hosler, R., White, B., Mohler, G.: Low cost gunshot detection using deep learning on the raspberry pi. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 3038–3044. IEEE (2019) Morehead, A., Ogden, L., Magee, G., Hosler, R., White, B., Mohler, G.: Low cost gunshot detection using deep learning on the raspberry pi. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 3038–3044. IEEE (2019)
12.
go back to reference Alsina-Pagès, R.M., Navarro, J., Alías, F., Hervás, M.: homesound: Real-time audio event detection based on high performance computing for behaviour and surveillance remote monitoring. Sensors 17(4), 854 (2017)CrossRef Alsina-Pagès, R.M., Navarro, J., Alías, F., Hervás, M.: homesound: Real-time audio event detection based on high performance computing for behaviour and surveillance remote monitoring. Sensors 17(4), 854 (2017)CrossRef
13.
go back to reference Wang, K., Yang, L., Yang, B.: Audio event detection and classification using extended R-FCN approach. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp. 128–132 (2017) Wang, K., Yang, L., Yang, B.: Audio event detection and classification using extended R-FCN approach. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp. 128–132 (2017)
14.
go back to reference Choi, I., Bae, S.H., Kim, N.S.: Deep convolutional neural network with structured prediction for weakly supervised audio event detection. Appl. Sci. 9(11), 2302 (2019)CrossRef Choi, I., Bae, S.H., Kim, N.S.: Deep convolutional neural network with structured prediction for weakly supervised audio event detection. Appl. Sci. 9(11), 2302 (2019)CrossRef
15.
go back to reference Romanov, S.A., Kharkovchuk, N.A., Sinelnikov, M.R., Abrash, M.R., Filinkov, V.: Development of an non-speech audio event detection system. In: 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1421–1423. IEEE (2020) Romanov, S.A., Kharkovchuk, N.A., Sinelnikov, M.R., Abrash, M.R., Filinkov, V.: Development of an non-speech audio event detection system. In: 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1421–1423. IEEE (2020)
18.
go back to reference Cao, Y., Iqbal, T., Kong, Q., Galindo, M., Wang, W., Plumbley, M.: Two-stage sound event localization and detection using intensity vector and generalized cross-correlation. DCASE2019 Challenge, Tech. Rep. (2019) Cao, Y., Iqbal, T., Kong, Q., Galindo, M., Wang, W., Plumbley, M.: Two-stage sound event localization and detection using intensity vector and generalized cross-correlation. DCASE2019 Challenge, Tech. Rep. (2019)
19.
go back to reference Cerutti, G., Prasad, R., Brutti, A., Farella, E.: Neural network distillation on IoT platforms for sound event detection. Proc. Interspeech 2019, 3609–3613 (2019) Cerutti, G., Prasad, R., Brutti, A., Farella, E.: Neural network distillation on IoT platforms for sound event detection. Proc. Interspeech 2019, 3609–3613 (2019)
20.
go back to reference Zinemanas, P., Cancela, P., Rocamora, M.: MAVD: A Dataset for Sound Event Detection in Urban Environments (2019) Zinemanas, P., Cancela, P., Rocamora, M.: MAVD: A Dataset for Sound Event Detection in Urban Environments (2019)
21.
go back to reference Wu, D.: An audio classification approach based on machine learning. In: 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 626–629. IEEE (2019) Wu, D.: An audio classification approach based on machine learning. In: 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 626–629. IEEE (2019)
22.
go back to reference Alías, F., Alsina-Pagès, R.M.: Review of wireless acoustic sensor networks for environmental noise monitoring in smart cities. J. Sens. 2019, 1–13 (2019)CrossRef Alías, F., Alsina-Pagès, R.M.: Review of wireless acoustic sensor networks for environmental noise monitoring in smart cities. J. Sens. 2019, 1–13 (2019)CrossRef
23.
go back to reference McFee, B., Salamon, J., Bello, J.P.: Adaptive pooling operators for weakly labeled sound event detection. IEEE/ACM Trans. Audio Speech Lang. Process. 26(11), 2180–2193 (2018)CrossRef McFee, B., Salamon, J., Bello, J.P.: Adaptive pooling operators for weakly labeled sound event detection. IEEE/ACM Trans. Audio Speech Lang. Process. 26(11), 2180–2193 (2018)CrossRef
Metadata
Title
Audio Surveillance: Detection of Audio-Based Emergency Situations
Authors
Zhandos Dosbayev
Rustam Abdrakhmanov
Oxana Akhmetova
Marat Nurtas
Zhalgasbek Iztayev
Lyazzat Zhaidakbaeva
Lazzat Shaimerdenova
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-88113-9_33

Premium Partner