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%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
