Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest

  • Marina YusoffEmail author
  • Amirul Sadikin Md. Afendi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)


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.


Audio classification Feature extraction Linear Predictive Coding Random forest Wildlife Conservation Society 



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.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Advanced Analytic Engineering Center (AAEC), Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia
  2. 2.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia

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