Show simple item record

dc.contributor.authorMporas, Ien
dc.contributor.authorPerikos, Ien
dc.contributor.authorKelefouras, Ven
dc.contributor.authorParaskevas, Men
dc.date.accessioned2021-01-26T16:42:43Z
dc.date.available2021-01-26T16:42:43Z
dc.date.issued2020-10-21en
dc.identifier.other0en
dc.identifier.urihttp://hdl.handle.net/10026.1/16818
dc.descriptionNo embargo required.en
dc.description.abstract

<jats:p>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.</jats:p>

en
dc.format.extent7379 - 7379en
dc.languageenen
dc.language.isoenen
dc.publisherMDPI AGen
dc.titleIllegal Logging Detection Based on Acoustic Surveillance of Foresten
dc.typeJournal Article
plymouth.issue20en
plymouth.volume10en
plymouth.publisher-urlhttps://www.mdpi.com/2076-3417/10/20/7379en
plymouth.journalApplied Sciencesen
dc.identifier.doi10.3390/app10207379en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-10-15en
dc.rights.embargodate2021-01-28en
dc.identifier.eissn2076-3417en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.3390/app10207379en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-10-21en
rioxxterms.typeJournal Article/Reviewen


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
@mire NV