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Robust ENF Estimation in Contaminated Audio
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AI
DOI：10.1109/TIFS.2025.3601405.png)
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Abstract
En 中文
Electric network frequency (ENF) is an important criterion in audio forensic analysis. However, environmental uncertainties often introduce various types of noises, diminishing the number of useful ENF samples in audio recordings. This issue is even more challenging in short-duration recordings. To address this issue, we propose an adaptive-window-based harmonic recombination (AWHR) method, which can accurately estimate ENF from noisy audio. Initially, we identify noisy samples and use a metric called noise ratio (NR) to determine the optimal harmonic. Adaptive windows are selectively applied to the noisy samples of the optimal harmonic to mitigate frequency spikes, prevent distortions, and preserve signal quality. This step also reduces computational complexity by minimizing the number of samples requiring enhancement. Finally, via a proposed harmonic recombination mechanism, we improve the number of useful ENF samples, which reduces the NR. Given the lack of ENF datasets designed to evaluate considerably contaminated audio, we have also built an ENF noisy audio harmonic (ENF-NAH) dataset. Experiments on public ENF-WHU and our ENF-NAH datasets show that the proposed AWHR method is effective in handling varying levels of contamination and is applicable to both long and short audio recordings.
Keywords:
Electric network frequency (ENF)
optimal harmonic
adaptive-window-based harmonic recombination (AWHR) method
noise ratio (NR)
adaptive window
ENF noisy audio harmonic (ENF-NAH) dataset
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