TY - JOUR
T1 - Illegal Logging Detection Based on Acoustic Surveillance of Forest
AU - Mporas, Iosif
AU - Perikos, Isidoros
AU - Kelefouras, Vasilios
AU - Paraskevas, Michael
PY - 2020/10/21
Y1 - 2020/10/21
N2 - 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.
AB - 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.
UR - https://pearl.plymouth.ac.uk/context/secam-research/article/2285/viewcontent/applsci_10_07379_v2.pdf
U2 - 10.3390/app10207379
DO - 10.3390/app10207379
M3 - Article
SN - 2076-3417
VL - 10
SP - 7379
EP - 7379
JO - Applied Sciences
JF - Applied Sciences
IS - 20
ER -