The Electric Network Frequency (ENF) serves as a unique signature inherent to
power distribution systems. Here, a novel approach for power grid
classification is developed, leveraging ENF. Spectrograms are generated from
audio and power recordings across different grids, revealing distinctive ENF
patterns that aid in grid classification through a fusion of classifiers. Four
traditional machine learning classifiers plus a Convolutional Neural Network
(CNN), optimized using Neural Architecture Search, are developed for One-vs-All
classification. This process generates numerous predictions per sample, which
are then compiled and used to train a shallow multi-label neural network
specifically designed to model the fusion process, ultimately leading to the
conclusive class prediction for each sample. Experimental findings reveal that
both validation and testing accuracy outperform those of current
state-of-the-art classifiers, underlining the effectiveness and robustness of
the proposed methodology.

通过从不同电网的音频和电能记录生成频谱图，利用特征明显的电网频率（ENF）模式辅助电网分类，本文提出了一种新的电网分类方法。该方法采用四种传统机器学习分类器和一个经由神经网络结构搜索优化的卷积神经网络（CNN）进行逐一分类，然后将这些分类结果综合到一个浅层多标签神经网络中进行训练，最终得出每个样本的确定性分类预测。实验结果表明，所提出的方法在验证和测试的准确率上超过了现有最先进的分类器，突显了其方法的有效性和鲁棒性。

基于电网频率使用多分类器融合框架的电网分类的频谱图分析

On Spectrogram Analysis in a Multiple Classifier Fusion Framework for  Power Grid Classification Using Electric Network Frequency

Electric Network Frequency (ENF) acts as a fingerprint in multimedia
forensics applications. In indoor environments, ENF variations affect the
intensity of light sources connected to power mains. Accordingly, the light
intensity variations captured by sensing devices can be exploited to estimate
the ENF. A first optical sensing device based on a photodiode is developed for
capturing ENF variations in indoor lighting environments. In addition, a device
that captures the ENF directly from power mains is implemented. This device
serves as a ground truth ENF collector. Video recordings captured by a camera
are also employed to estimate the ENF. The camera serves as a second optical
sensor. The factors affecting the ENF estimation are thoroughly studied. The
maximum correlation coefficient between the ENF estimated by the two optical
sensors and that estimated directly from power mains is used to measure the
estimation accuracy. The paper's major contribution is in the disclosure of
extensive experimental evidence on ENF estimation in scenes ranging from static
ones capturing a white wall to non-static ones, including human activity.

电网频率（ENF）在多媒体取证应用中具有指纹的作用。本文研究了在室内环境中，ENF 的变化如何影响连接到电源的光源的强度，并利用感应设备捕捉到的光强度变化来估计 ENF。同时，本文开发了一个基于光电二极管的第一种光学传感设备，用于捕捉室内照明环境中的 ENF 变化，实现了直接从电源中捕捉 ENF 的设备，用作 ENF 的真实采集器，还利用摄像机记录的视频估计 ENF，探讨了影响 ENF 估计的因素，并利用两种光学传感器对比以及直接从电源中估计的 ENF 之间的最大相关系数来衡量估计精度。该论文的重要贡献在于揭示了在静态场景（如白墙）到包括人类活动的非静态场景中进行 ENF 估计的广泛实验论证。