Skip to main content

Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest

  • Conference paper
  • First Online:
Soft Computing in Data Science (SCDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 937))

Included in the following conference series:

  • 771 Accesses

Abstract

Endangered wildlife is protected in remote land where people are restricted to enter. But intrusions of poachers and illegal loggers still occur due to lack of surveillance to cover a huge amount of land. The current usage of stealth ability of the camera is low due to limitations of camera angle of view. Maintenance such as changing batteries and memory cards were troublesome reported by Wildlife Conservation Society, Malaysia. Remote location with no cellular network access would be difficult to transmit video data. Rangers need a system to react to intrusion on time. This paper aims to address the development of an audio events recognition for intrusion detection based on the vehicle engine, wildlife environmental noise and chainsaw activities. Random Forest classification and feature extraction of Linear Predictive Coding were employed. Training and testing data sets used were obtained from Wildlife Conservation Society Malaysia. The findings demonstrate that the accuracy rates achieve up to 86% for indicating an intrusion via audio recognition. It is a good attempt as a primary study for the classification of a real data set of intruders. This intrusion detection will be beneficial for wildlife protection agencies in maintaining security as it is less power consuming than the current camera trapping surveillance technique.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wildlife.gov.my: Latar Belakang PERHILITAN. http://www.wildlife.gov.my/index.php/2016-04-11-03-50-17/2016-04-11-03-57-37/latar-belakang. Accessed 30 Apr 2018

  2. Pei, L.G.: Southeast Asia marks progress in combating illegal timber trade. http://www.flegt.org/news/content/viewItem/southeast-asia-marks-progress-in-combating-illegal-timber-trade/04-01-2017/75. Accessed 30 Apr 2018

  3. Inus, K.: Special armed wildlife enforcement team to be set up to counter poachers, 05 November 2017. https://www.nst.com.my/news/nation/2017/10/294584/special-armed-wildlife-enforcement-team-be-set-counter-poachers. Accessed 30 June 2018

  4. Kamminga, J., Ayele, E., Meratnia, N., Havinga, P.: Poaching detection technologies—a survey. Sensors 18(5), 1474 (2018)

    Article  Google Scholar 

  5. Ariffin, M.: Enforcement against wildlife crimes in west Malaysia: the challenges. J. Sustain. Sci. Manag. 10(1), 19–26 (2015)

    MathSciNet  Google Scholar 

  6. Davis, D., Lisiewski, B.: U.S. Patent Application No. 15/296, 136 (2018)

    Google Scholar 

  7. Davis, E.: New Study Shows Over a Third of Protected Areas Surveyed are Severely at Risk of Losing Tigers, 04 April (2018). https://www.worldwildlife.org/press-releases/new-study-shows-over-a-third-of-protected-areas-surveyed-are-severely-at-risk-of-losing-tigers. Accessed 30 June 2018

  8. Mac Aodha, O., et al.: Bat detective—deep learning tools for bat acoustic signal detection. PLoS computational Biol. 14(3), e1005995 (2018)

    Article  Google Scholar 

  9. Maijala, P., Shuyang, Z., Heittola, T., Virtanen, T.: Environmental noise monitoring using source classification in sensors. Appl. Acoust. 129, 258–267 (2018)

    Article  Google Scholar 

  10. Zhu, B., Xu, K., Wang, D., Zhang, L., Li, B., Peng, Y.: Environmental Sound Classification Based on Multi-temporal Resolution CNN Network Combining with Multi-level Features. arXiv preprint arXiv:1805.09752 (2018)

  11. Valada, A., Spinello, L., Burgard, W.: Deep feature learning for acoustics-based terrain classification. In: Bicchi, A., Burgard, W. (eds.) Robotics Research. SPAR, vol. 3, pp. 21–37. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60916-4_2

    Chapter  Google Scholar 

  12. Heittola, T., Çakır, E., Virtanen, T.: The machine learning approach for analysis of sound scenes and events. In: Virtanen, T., Plumbley, M., Ellis, D. (eds.) Computational Analysis of Sound Scenes and Events, pp. 13–40. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63450-0_2

    Chapter  Google Scholar 

  13. Hamzah, R., Jamil, N., Seman, N., Ardi, N, Doraisamy, S.C.: Impact of acoustical voice activity detection on spontaneous filled pause classification. In: Open Systems (ICOS), pp. 1–6. IEEE (2014)

    Google Scholar 

  14. Seman, N., Roslan, R., Jamil, N., Ardi, N.: Bimodality streams integration for audio-visual speech recognition systems. In: Abraham, A., Han, S.Y., Al-Sharhan, S.A., Liu, H. (eds.) Hybrid Intelligent Systems. AISC, vol. 420, pp. 127–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27221-4_11

    Chapter  Google Scholar 

  15. Seman, N., Jusoff, K.: Acoustic pronunciation variations modeling for standard Malay speech recognition. Comput. Inf. Sci. 1(4), 112 (2008)

    Google Scholar 

  16. Dlir, A., Beheshti, A.A., Masoom, M.H.: Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors. arXiv preprint arXiv:1804.01212 (2018)

  17. Aljaafreh, A., Dong, L.: An evaluation of feature extraction methods for vehicle classification based on acoustic signals. In: 2010 International Conference on Networking, Sensing and Control (ICNSC), pp. 570–575. IEEE (2010)

    Google Scholar 

  18. Baelde, M., Biernacki, C., Greff, R.: A mixture model-based real-time audio sources classification method. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2427–2431. IEEE (2017)

    Google Scholar 

  19. Dilber, D.: Feature Selection and Extraction of Audio, pp. 3148–3155 (2016). https://doi.org/10.15680/IJIRSET.2016.0503064. Accessed 30 Apr 2018

  20. Xia, X., Togneri, R., Sokel, F., Huang, D.: Random forest classification based acoustic event detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 163–168. IEEE (2017)

    Google Scholar 

  21. Lu, L., Jiang, H., Zhang, H.: A robust audio classification and segmentation method. In: Proceedings of the Ninth ACM International Conference on Multimedia, pp. 203–211. ACM (2001)

    Google Scholar 

  22. Anselam, A.S., Pillai, S.S.: Performance evaluation of code excited linear prediction speech coders at various bit rates. In: 2014 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), April 2014, pp. 93–98. IEEE (2014)

    Google Scholar 

  23. Chamoli, A., Semwal, A., Saikia, N.: Detection of emotion in analysis of speech using linear predictive coding techniques (LPC). In: 2017 International Conference on Inventive Systems and Control (ICISC), pp. 1–4. IEEE (2017)

    Google Scholar 

  24. Grama, L., Buhuş, E.R., Rusu, C.: Acoustic classification using linear predictive coding for wildlife detection systems. In: 2017 International Symposium on Signals, Circuits and Systems (ISSCS), pp. 1–4. IEEE (2017)

    Google Scholar 

  25. Homburg, H., Mierswa, I., Möller, B., Morik, K., Wurst, M.: A benchmark dataset for audio classification and clustering. In: ISMIR, September 2005, vol. 2005, pp. 528–531 (2005)

    Google Scholar 

  26. Jaiswal, J.K., Samikannu, R.: Application of random forest algorithm on feature subset selection and classification and regression. In: 2017 World Congress on Computing and Communication Technologies (WCCCT), pp. 65–68. IEEE (2017)

    Google Scholar 

  27. Kumar, S.S., Shaikh, T.: Empirical evaluation of the performance of feature selection approaches on random forest. In: 2017 International Conference on Computer and Applications (ICCA), pp. 227–231. IEEE (2017)

    Google Scholar 

  28. Tang, Y., Liu, Q., Wang, W., Cox, T.J.: A non-intrusive method for estimating binaural speech intelligibility from noise-corrupted signals captured by a pair of microphones. Speech Commun. 96, 116–128 (2018)

    Article  Google Scholar 

  29. Balili, C.C., Sobrepena, M.C.C., Naval, P.C.: Classification of heart sounds using discrete and continuous wavelet transform and random forests. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 655–659. IEEE (2015)

    Google Scholar 

  30. Denil, M., Matheson, D., De Freitas, N.: Narrowing the gap: random forests in theory and in practice. In: International Conference on Machine Learning, January 2014, pp. 665–673 (2014)

    Google Scholar 

  31. Behnamian, A., Millard, K., Banks, S.N., White, L., Richardson, M., Pasher, J.: A systematic approach for variable selection with random forests: achieving stable variable importance values. IEEE Geosci. Remote Sens. Lett. 14(11), 1988–1992 (2017)

    Article  Google Scholar 

  32. Biau, G.L., Curie, M., Bo, P.V.I., Cedex, P., Yu, B.: Analysis of a random forests model. J. Mach. Learn. Res. 13, 1063–1095 (2012)

    MathSciNet  Google Scholar 

  33. Phan, H., et al.: Random regression forests for acoustic event detection and classification. IEEE/ACM Trans. Audio Speech Lang. Process. 23(1), 20–31 (2015)

    Article  Google Scholar 

  34. Xu, Y.: Research and implementation of improved random forest algorithm based on Spark. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 499–503. IEEE (2017)

    Google Scholar 

  35. Zhang, Z., Li, Y., Zhu, X., Lin, Y.: A method for modulation recognition based on entropy features and random forest. In: IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 243–246. IEEE (2017)

    Google Scholar 

  36. Abuella, M., Chowdhury, B.: Random forest ensemble of support vector regression models for solar power forecasting. In: Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5. IEEE (2017)

    Google Scholar 

  37. Manzoor, M.A., Morgan, Y.: Vehicle make and model recognition using random forest classification for intelligent transportation systems. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 148–154. IEEE (2018)

    Google Scholar 

Download references

Acknowledgement

The authors express a deep appreciation to the Ministry of Education, Malaysia for the grant of 600-RMI/FRGS 5/3 (0002/2016), Institute of Research and Innovation, Universiti Teknologi MARA and the Information System Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia for providing essential support and knowledge for the work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marina Yusoff .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yusoff, M., Md. Afendi, A.S. (2019). Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3441-2_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3440-5

  • Online ISBN: 978-981-13-3441-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics