ORCID
- Kelefouras, Vasilios: 0000-0001-9591-913X
Abstract
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.
DOI
10.3390/app10207379
Publication Date
2020-10-21
Publication Title
Applied Sciences
Volume
10
Issue
20
Embargo Period
2021-01-28
Organisational Unit
School of Engineering, Computing and Mathematics
First Page
7379
Last Page
7379
Recommended Citation
Mporas, I., Perikos, I., Kelefouras, V., & Paraskevas, M. (2020) 'Illegal Logging Detection Based on Acoustic Surveillance of Forest', Applied Sciences, 10(20), pp. 7379-7379. Available at: https://doi.org/10.3390/app10207379
