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Open AccessArticle

Illegal Logging Detection Based on Acoustic Surveillance of Forest

1
School of Physics, Engineering and Computer Science, College Lane Campus, University of Hertfordshire, Hatfield AL10 9AB, UK
2
Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
3
Computer Technology Institute and Press “Diophantus”, 26504 Patras, Greece
4
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
5
Department of Electrical and Computer Engineering, University of the Peloponnese, 26334 Patras, Greece
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(20), 7379; https://doi.org/10.3390/app10207379
Received: 4 September 2020 / Revised: 12 October 2020 / Accepted: 15 October 2020 / Published: 21 October 2020
(This article belongs to the Section Acoustics and Vibrations)
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. View Full-Text
Keywords: acoustic surveillance; binary classification; intelligent monitoring systems; machine learning; audio processing acoustic surveillance; binary classification; intelligent monitoring systems; machine learning; audio processing
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MDPI and ACS Style

Mporas, I.; Perikos, I.; Kelefouras, V.; Paraskevas, M. Illegal Logging Detection Based on Acoustic Surveillance of Forest. Appl. Sci. 2020, 10, 7379. https://doi.org/10.3390/app10207379

AMA Style

Mporas I, Perikos I, Kelefouras V, Paraskevas M. Illegal Logging Detection Based on Acoustic Surveillance of Forest. Applied Sciences. 2020; 10(20):7379. https://doi.org/10.3390/app10207379

Chicago/Turabian Style

Mporas, Iosif; Perikos, Isidoros; Kelefouras, Vasilios; Paraskevas, Michael. 2020. "Illegal Logging Detection Based on Acoustic Surveillance of Forest" Appl. Sci. 10, no. 20: 7379. https://doi.org/10.3390/app10207379

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