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

TL;DR

This paper introduces an efficient Capon spectral estimator with temporal windowing for accurate Electric Network Frequency estimation, outperforming existing methods especially with short time windows in multimedia forensics.

Contribution

It presents a novel ENF estimation approach using a filter-bank Capon spectral estimator with flexible temporal windowing and fast matrix inversion techniques, improving accuracy and efficiency.

Findings

Outperforms state-of-the-art ENF estimation methods with 1-second windows.

Highly accurate results in speech recordings.

Temporal windowing significantly improves estimation accuracy.

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.