Efficient Capon-Based Approach Exploiting Temporal Windowing for Electric Network Frequency Estimation

Abstract

Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation. Krylov matrices are employed for fast implementation of matrix inversions. The proposed approach outperforms the state-of-the-art methods in ENF estimation, when a short time window of 1 second is employed in power recordings. In speech recordings, the proposed approach yields highly accurate results with respect to both time complexity and accuracy. Moreover, the impact of different temporal windows is studied. The results show that even the most trivial methods for ENF estimation, such as the Short-Time Fourier Transform, can provide better results than the most recent state-of-the-art methods, when a temporal window is employed. The correlation coefficient is used to measure the ENF estimation accuracy.

Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

“…Window selection can significantly improve accuracy without affecting time requirements at all. A novel accurate approach employing a fast Capon-based spectral estimator after applying a temporal Parzen window was proposed in [40]. In most cases, the ENF signal in audio recordings suffers from strong interferences.…”

“…The choice of temporal window is of crucial importance in ENF estimation. In [40], extensive experiments were conducted in order to determine the best window choice. Next, the power spectrum is estimated KARANTAIDIS AND KOTROPOULOS -399…”

“…Here, two benchmark data sets (http://www.sal.ufl.edu/download.html) provided by the University of Florida are employed in order to evaluate the proposed approach. These data sets consist of real‐valued signals and were first discussed in [28] and have been employed also in [29, 35, 36, 40].…”

“…The choice of temporal window is of crucial importance in ENF estimation. In [40], extensive experiments were conducted in order to determine the best window choice. Next, the power spectrum is estimated for each frame and an approximate frequency ω qmax associated with the maximum power spectrum magnitude is extracted.…”

An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

“…In the pre-processing stage, signal filtering and temporal window choice were found to be critical in delivering accurate estimation results. A fast version of Capon spectral estimator based on Gohberg-Semencul factorization was presented in [12]. That method along with the use of a Parzen temporal window led to accurate ENF estimation.…”