Illegal logging detection based on acoustic surveillance of forest

MPORAS, I., PERIKOS, I., KELEFOURAS, Vasileios and PARASKEVAS, M. (2020). Illegal logging detection based on acoustic surveillance of forest. Applied Sciences (Switzerland), 10 (20), 1-12.

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Abstract

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB.

Item Type:	Article
Identification Number:	https://doi.org/10.3390/app10207379
Page Range:	1-12
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	23 Nov 2020 17:01
Last Modified:	17 Mar 2021 20:15
URI:	https://shura.shu.ac.uk/id/eprint/27635

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