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Detection of impostor and tampered segments in audio by using an intelligent system

Mubeen, Zeshan and Afzal, Mehtab and Ali, Zulfiqar and Khan, Suleman and Imran, Muhammad (2021) 'Detection of impostor and tampered segments in audio by using an intelligent system.' Computers and Electrical Engineering, 91. ISSN 0045-7906

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The transmission of audio data via the Internet of Things makes such data vulnerable to tampering. Moreover, the availability of sophisticated tampering tools has allowed mobsters to change the context of audio data by altering their segments. Tampered audio may result in unpleasant situations for any member of society. To avoid such circumstances, a new audio forgery detection system is proposed in this study. This system can be deployed in edge devices to identify impostors and tampering in audio data. The proposed system is implemented using state-of-the-art mel-frequency cepstral coefficient features. Meanwhile, a Gaussian mixture model is used to train and validate the system. To evaluate the proposed system, a dataset of tampered audios is created by mixing recordings from two different speakers. The performance of the proposed system in authenticating genuine audio is between 92.50% and 100%, and that in detecting forged audio is between 99.90 and 100%.

Item Type: Article
Uncontrolled Keywords: Audio forgery, Splicing, Audio forensic, Zedge computing, Machine learning, Voice activity detection, Audio authentication
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 20 May 2021 09:04
Last Modified: 20 May 2021 09:15

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