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Edited audio detection using ensemble learning
Author(s)
Date Issued
February 27, 2015
Type
Conference Paper
Abstract
Detecting 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.
Citation
Proceedings of the 2015 7th International Conference on Knowledge and Smart Technology Kst 2015, 71-74, 2015