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.

Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, scene diversity, motion interference, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we formulate and validate the physical mechanism for the ENF captured in events and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. To validate the effectiveness, we build the first Event-Video ENF Dataset (EV-ENFD) and its extension EV-ENFD+ with diverse scenarios, including static, dynamic, and extreme lighting scenes. Comprehensive experiments have been conducted on our proposed datasets, showcasing that our proposed E-ENF significantly outperforms the V-ENF in extracting accurate ENF traces, especially in challenging environments.

Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF)from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely re-lies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, motion, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we first formulate and validate the physical mechanism for the ENF captured in events, and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. Furthermore, we build an Event-Video ENF Dataset (EV-ENFD) that records both events and videos in diverse scenes. Extensive experiments on EV-ENFD demonstrate that our proposed E-ENF method can extract more accurate ENF traces, outperforming the conventional V-ENF by a large margin, especially in challenging environments with object motions and extreme lighting conditions. The code and dataset are available at https://github.com/x1x-creater/E-ENF.