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This report presents the results to the challenge of
”Exploring Power Signatures for Location Forensics of Media
Recordings” as apart of Signal Processing Cup 2016 by IEEE
Signal Processing Society. Here we examine different frequency
estimation and classification techniques to provide accurate ENF
estimates and classify these signals into the corresponding grid
of recording. In this report we propose methods of efficient
extraction of ENF signal using quadratic interpolation and
frequency tracking.The SVM and GMM classifiers used provided

Electrical network frequency (ENF) has been used as evidence for location forensic. To determine location, we need accurate ENF information from noisy media files, select features of the ENF signal and the classify it based on previous knowledge of different grids.

The Electric network frequency (ENF) signal is a unique signal for different parts of the world. It is captured by electric devices, and can be used in authentication and automatic synchronization of digital media recordings. In this paper we propose an algorithm to extract ENF from power and audio recordings, and use ENF criterion to identify the region-of-recording. We also propose a design of a circuit to record the electrical power grid.

Power grids are available all over the world for their necessity in all our everyday activities. Each power grid has its own electrical network frequency pattern which is considered as a signature or a finger print of this power grid. Any audio/video recorded in a power grid, whether directly connected to the power mains or not, is affected by the ENF signature of this grid. Post processing could take place to extract the ENF pattern from recordings which can be used in localization of different media signals recorded among grids.

At the intersection of signal processing and information forensics, the Signal Processing Cup 2016 global competition has explored a time-varying location-dependent signature of power grids that can be intrinsically captured in media recordings. This signature is called the Electric Network Frequency (ENF) signals. Throughout the SP Cup 2016 competition, participants were provided with multiple training, practice, and testing datasets that consisted of recordings made in different grids and containing ENF traces.