Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/4409
Nаziv: | Improving location of recording classification using Electric Network Frequency (ENF) analysis | Аutоri: | Saric Z. Žunić, Antonije Zrnić, Kristiana Knežev, Miloš Despotovic D. Delić, Tijana |
Dаtum izdаvаnjа: | 19-окт-2016 | Čаsоpis: | SISY 2016 - IEEE 14th International Symposium on Intelligent Systems and Informatics, Proceedings | Sažetak: | © 2016 IEEE. 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. | URI: | https://open.uns.ac.rs/handle/123456789/4409 | ISBN: | 9781509028665 | DOI: | 10.1109/SISY.2016.7601517 |
Nаlаzi sе u kоlеkciјаmа: | POLJF Publikacije/Publications |
Prikаzаti cеlоkupаn zаpis stаvki
SCOPUSTM
Nаvоđеnjа
5
prоvеrеnо 03.05.2024.
Prеglеd/i stаnicа
15
Prоtеklа nеdеljа
3
3
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.