# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 1 Introduction  
Power system frequency yields to instantaneous changes in accordance with load variations and control methodologies. Electrical network frequency (ENF) is the base frequency (50 or 60 Hz) of a power distribution network and ENF sequences are generated due to the fluctuations in frequency from the nominal ENF value. These ENF sequences contain recognizable patterns of a power grid, since the ENF variations are considered to be uniform for a particular grid and are separable from grid-to-grid observation. When a multimedia signal like an audio signal is recorded close to a grid or directly from the power supply line, power signatures of that specific grid location are embedded into the audio recording due to the electromagnetic interference (EMI). Thereby, the audio recording can be applied for location forensic analysis in the situation when the concurrent power recording is absent. Since ENF sequences carry the power signatures of a distribution grid, audio authenticity can be tested for location-stamp verification by extracting and analyzing the ENF signals of the recordings. Location-stamp investigation yields to a significant tool for anti-terrorist drives and for preventing and prosecuting cyber crimes. In this article, ENF signals are extracted from a number of digital recordings of power and audio signals captured in different grid locations and then a classification model is developed based on some potential feature vectors of the estimated ENF patterns. A comprehensive ENF analysis of digital audio recordings for forensics and security applications is articulated in Reference [1]. A thorough study on the factors affecting the capture of ENF sequences in audio recordings is documented in Reference [2]. However, a number of novel methodologies in regard to ENF extraction from power and multimedia (audio or video) signals are reported in References [3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. In this framework, Root MUSIC algorithm [5] is applied to extract ENF sequences from the digital recordings. Root MUSIC algorithm provides quite a high precision with a moderate computational cost to determine ENF signals. As an intrinsic investigation of ENF signals, these sequences are analyzed based on the fluctuation trends with respect to the nominal ENF values. In this follow-up, the extracted ENF signals are decomposed into low outliers and high outliers frequency segments, where low outliers segments show less fluctuations in frequency value from the nominal ENF than the high outliers segments. Then, applying statistics and signal processing methods a number of potential feature components are determined separately from the low outliers and high outliers ENF signals. A multi-class support vector machine (SVM) classifier is developed to locate the sources of recordings. However, a number of ENF based location forensics analyses are proposed in References [13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. The reported ENF based location-stamp verification framework presents a decomposition of ENF sequences of digital power and audio signals considering fluctuation trends. Then potential features are experimentally extracted from the decomposed ENF sequences and to verify the quality of the features, Euclidean distance matrices are applied. The power and multimedia signals localization methods reported in the previous works do not consider the implicit analysis based on decomposition of the extracted ENF sequences in terms of frequency fluctuation trends. The presented outliers determination and potential features extraction from the decomposed ENF signals yield better understanding of frequency behavior of power and multimedia recordings collected from different grid locations around the world. In addition, an intrinsic experimental approach is carried out to extract potential feature vectors, which can be the basis for an efficient classification model. The quality of the proposed features is ensured as well. Thereby, the features extraction process represents a unique premise of the presented work.