Author : Majed El Helou
Affiliation : American University of Beirut
Country : Lebanon
Category : Computer Science & Information Technology
Volume, Issue,
Month, Year
: 6, 5, November, 2016
ABSTRACT
The
electric network frequency (ENF) of power lines leaves its trace in nearby
media recordings. The ENF signals vary in a consistent way in a given power
grid. Therefore, it is possible to develop signal processing and machine
learning techniques to identify the grid of origin by extracting attributes of
the embedded ENF signal in recorded audio. This paper presents a model based on
a novel ENF extraction technique with training on audio and power recordings
from different grids. The proposed approach is based on correcting erroneously
selected peaks from the Short Time Fourier Transform (STFT) by leveraging time
correlations. These peaks are mistakenly taken for the frequency component
belonging to the embedded ENF signal of the power grid and are corrected by the
algorithm. Results on a test set of 50 recordings from nine different locations
demonstrate the effectiveness of the proposed approach with an overall accuracy
of 88%
Keyword : ENF, Region of Recording, Media Recordings,
Power Grids, Noise in Audio Recordings.
For More Details : https://airccj.org/CSCP/vol6/csit65210.pdf
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