Edited audio detection using ensemble learning

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dc.contributor.authorSuwan, Takdanai
dc.contributor.authorJaiyen, Saichon
dc.contributor.authorWiangsripanawan, Rungrat
dc.date.accessioned2026-08-06T10:11:21Z
dc.date.available2026-08-06T10:11:21Z
dc.date.issued2015-02-27
dc.description.abstractDetecting edited audios is the challenging problem that can help forensic scientists to separate genuine, unedited recording from edited recordings. This paper proposes the technique for detecting edited audios using Ensemble Learning. This problem can be considered as a two-class classification problem which audio data are classified into two classes including edited and unedited audios. The performance of the proposed model is compared with the performance from the Support Vector Machine, Naïve Bayes, Radial Basis Function Neural Network, and Probabilistic Neural Networks. The experimental results demonstrate that the proposed model is the most appropriated method for detecting the edited audios.
dc.identifier.citationProceedings of the 2015 7th International Conference on Knowledge and Smart Technology Kst 2015, 71-74, 2015
dc.identifier.doi10.1109/KST.2015.7051474
dc.identifier.other2-s2.0-84925865753
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/6190
dc.sourceProceedings of the 2015 7th International Conference on Knowledge and Smart Technology Kst 2015
dc.subjectAdaboost
dc.subjectAudio
dc.subjectBoosting Ensemble
dc.subjectClassification
dc.subjectNaive Bayes
dc.subjectSupport Vector Machine
dc.subjectSVM
dc.titleEdited audio detection using ensemble learning
dc.typeConference Paper

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