Abstract
Recently the Electric Network Frequency (ENF), one of the main traits of a power grid, had become increasingly popular in forensics since it is considered as a signature in multimedia recordings. By analyzing the ENF, it is possible to determine the time and location of a recording. In this paper, the ENF signals were classified using five different machine learning algorithms in order to detect the region of the origin of the ENF signals extracted from power and audio recordings coming from 10 different electric networks. Three sets of novel signal features are introduced and compared with the ones previously discussed in the literature. The improvement in the classification accuracy when a combination of the referent and novel feature sets was used ranges from 3% to 19% for the ENF signals extracted from power and audio recordings, respectively. Finally, the classifier with the highest achieved average accuracy was found to be Random Forest.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
