  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.
