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dc.contributor.authorMporas, Iosif
dc.contributor.authorPerikos, Isidoros
dc.contributor.authorKelefouras, Vasilios
dc.contributor.authorParaskevas, Michael
dc.date.accessioned2020-10-23T12:15:01Z
dc.date.available2020-10-23T12:15:01Z
dc.date.issued2020-10-21
dc.identifier.citationMporas , I , Perikos , I , Kelefouras , V & Paraskevas , M 2020 , ' Illegal Logging Detection Based on Acoustic Surveillance of Forest ' , Applied Sciences , vol. 10 , no. 20 , 7379 . https://doi.org/10.3390/app10207379
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 22860632
dc.identifier.otherPURE UUID: 032f2e2e-429f-47ed-b1d5-411240c6f105
dc.identifier.otherScopus: 85093982510
dc.identifier.urihttp://hdl.handle.net/2299/23317
dc.description© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.description.abstractIn 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.en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofApplied Sciences
dc.rightsOpen
dc.titleIllegal Logging Detection Based on Acoustic Surveillance of Foresten
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2020-10-21
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3390/app10207379
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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