Illegal Logging Detection Based on Acoustic Surveillance of Forest

Applied Sciences

Isidoros Perikos¹  Michael Paraskevas²  Vasilios Kelefouras³  Iosif Mporas⁴ 

[1] Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece;Computer Technology Institute and Press “Diophantus”, 26504 Patras, Greece;School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK;School of Physics, Engineering and Computer Science, College Lane Campus, University of Hertfordshire, Hatfield AL10 9AB, UK;

关键词: acoustic surveillance;    binary classification;    intelligent monitoring systems;    machine learning;    audio processing;   

DOI  :  10.3390/app10207379

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown