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        Illegal Logging Detection Based on Acoustic Surveillance of Forest

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        Author
        Mporas, Iosif
        Perikos, Isidoros
        Kelefouras, Vasilios
        Paraskevas, Michael
        Attention
        2299/23317
        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.
        Publication date
        2020-10-21
        Published in
        Applied Sciences
        Published version
        https://doi.org/10.3390/app10207379
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/23317
        Relations
        School of Physics, Engineering & Computer Science
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