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We report a multi-harmonic histogram method for extracting and analyzing electric network frequency (ENF) signals to identify power grids. Given a voltage-time measurement of a power grid with a base frequency f0, we compute the ENF signals at multiple harmonic locations f_0 and extract (i) a histogram of the magnitudes of the ENF; (ii) a histogram of the signal power and noise power surrounding the ENF; (iii) a histogram of the signal-to noise-ratio (SNR) of the ENF.

Extracting the Electric Network Frequency (ENF) fluctuations from an audio recording and comparing it to a reference database is a new approach in performing forensic digital audio authentication. The problem statement of the IEEE SP cup 2016 competition relates to time-varying location-dependent signature of power grids as it becomes intrinsically captured in media recordings, due to direct or indirect influences from the respective power grid. In this project signal processing and information security/forensics are collectively elaborated.

Electric Network Frequency is the frequency of power distribution networks in power grids that fluctuates about a nominal value with respect to the changing loads.Its ubiquitous nature has made notable contributions to forensic analysis that has substantiated its use as a significant tool in this area. In this paper we have proposed a technique to identify the power grid in which the ENF containing signal was recorded, without the assistance of concurrent power references.

This report describes a method which is implemented for the IEEE signal Processing Cup 2016, that is to extract power signatures from a given media signal and identify where the signal was recorded in order to be used for forensic applications.
The primary task of this project was to design a system which can identify the captured location of a given multimedia sample based on the Electrical network frequency signals (ENF) signal embedded in it.

Electric Network Frequency(ENF) is a recently developed technique for the authentication of audio signals. ENF gets embedded in audio signals due to electromagnetic interferences from power lines and hence can be used as a measure to find the geographical location and time of recording. Given an audio signal, this paper presents a technique of finding the power grid the audio belongs to. Towards this, we first extract the ENF sinusoid using a very narrow bandwidth filter centered around the nominal frequency. The filter is designed using a frequency response masking approach.

We present two contributions in this work: i)Novel electric network frequency (ENF) classification algorithm, and ii)Circuit for measuring power signals from the power grid.We first propose a novel ENF signal estimation algorithm.This algorithm explicitly makes use of the harmonic information present in the signal and estimates the nominal frequency based on the most reliable harmonic. The ENF signal is estimated from the most reliable harmonic by employing a Gaussian weighting window to mitigate the effects of noise. We