# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 2 Enf Extraction and Database Formation Based on Location Specific Recordings
### Extraction of Enf Sequences
#### 2.2.2 Enf Sequences of Audio Recordings  
**Step-1:** Each 5 min long audio signal is processed through a 2 nd order Butterworth band-pass filter designed with a frequency band of [\(F_{c}-F_{b}\), \(F_{c}+F_{b}\)] Hz. The filtered signal is segmented into 15 s long time frames, which overlap with each of the corresponding previous frames for 10 s. Thereby, It can be implied that the overlapping of a time frame with its previous frame is about 66.67%.  
**Step-2:** Root MUSIC algorithm is applied to extract the ENF sequence of each 5 s long signal segment. Thus, the power signatures embedded in an audio recording are estimated.  
Figures 1-4 present the sample ENF sequences extracted from power and audio recordings from different grid locations. The variations in frequency depend on the load fluctuations and power system control methodologies. Loads change from time to time during operation and thus  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Grid Locations** & **Grid Names** \\ \hline
1 & Texas & \(A\) \\
2 & Lebanon & \(B\) \\
3 & Eastern U.S. & C \\
4 & Turkey & \(D\) \\
5 & Ireland & \(E\) \\
6 & France & \(F\) \\
7 & Tenerife & G \\
8 & India & \(H\) \\
9 & Western U.S. & \(I\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid Location-Stamp Information of the Training Dataset.  
the system frequency experiences variations accordingly. If the control techniques are effective and reliable, there are less fluctuations in frequency and less instabilities are observed in a power system network. Thus the ENF patterns can be viewed as a stability index of a particular power distribution grid. From Figure 1 it can be implied that the approximated maximum ENF variations from the nominal 60 Hz value for power recordings collected from Texas, Eastern U.S. and Western U.S. lie within the boundaries of [+0.02, \(-\)0.04] Hz, [+0.024, \(-\)0.03] Hz and [+0.025, \(-\)0.01] Hz respectively. However, from Figure 2 it can be observed that the approximated maximum ENF fluctuations from the nominal 60 Hz value for audio recordings collected from Texas, Eastern U.S. and Western U.S. lie within the boundaries of [+0.05, \(-\)0.05] Hz, [+0.05, \(-\)0.058] Hz and [+0.1, \(-\)0.2] Hz respectively. From Figure 3 it can be observed from this particular recorded power sample that the grid frequency in Lebanon is not stabilized at the desired 50 Hz value and the ENF fluctuations are quite high, whereas the approximated maximum ENF variations in Turkey, Ireland, France, Tenerife and India lie within the boundaries of [+0.03, \(-\)0.04] Hz, [+0.051, \(-\)0.03] Hz, [+0.018, \(-\)0.02] Hz, [+0.13, \(-\)0.04] Hz and [+0.15, \(-\)0.25] Hz respectively. From Figure 4 it can be observed from this particular recorded audio sample that the nominal frequency in Ireland is not stabilized at the desired 50 Hz value and the peak fluctuation in ENF is almost 0.15 Hz, whereas the approximated maximum ranges of ENF variations in Lebanon, Turkey, France, Tenerife and India are [+0.37, \(-\)0.04] Hz, [+0.07, \(-\)0.052] Hz, [+0.04, \(-\)0.08] Hz, [0, \(-\)0.16] Hz and [+0.26, \(-\)0.05] Hz respectively. From the above analysis based on ENF variations it can be yielded that the 60 Hz grids have better controlled and stable power systems to mitigate frequency fluctuations in comparison with the 50 Hz grids. However, the three 60 Hz grid locations are mainly in United States of America. Among the six 50 Hz power grids, France has the least ENF variations.  
Figure 1: Sample electrical network frequency (ENF) sequences extracted from power recordings from different 60 Hz grid locations.