On Spectrogram Analysis in a Multiple Classifier Fusion Framework for
  Power Grid Classification Using Electric Network Frequency
Georgios Tzolopoulos Christos Korgialas Constantine Kotropoulos
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.