Assessing Spectral Estimation Methods For Electric Network Frequency Extraction

The Electric Network Frequency (ENF) criterion provides useful forensic evidence for multimedia authentication. In this paper, a systematic study of non-parametric and parametric spectral estimation methods is conducted for ENF extraction. Fast implementations of the Capon method and the Iterative Adaptive Approach, which exploit the Gohberg-Semencul factorization of the inverse covariance matrix, are included as well. When long segments are used, a very high matching accuracy is achieved. That is, the maximum correlation-coefficient between the extracted ENF and the ground truth may exceed 99%. Similarly, the standard deviation of error may be as small as 1.069 . 10(-3). Non-parametric spectral estimation techniques are shown to be able to detect an alteration in an audio recording, where a short utterance recorded in Europe is replaced by the same content recorded in the US.