Home // SIGNAL 2025, The Tenth International Conference on Advances in Signal, Image and Video Processing // View article
Authors:
Christos Korgialas
Ioannis Tsingalis
Georgios Tzolopoulos
Constantine Kotropoulos
Keywords: electric network frequency (ENF); grid location estimation; audio processing; multimedia forensics
Abstract:
A novel framework, called InterGridNet, is introduced, leveraging a shallow RawNet model for geolocation classification of Electric Network Frequency (ENF) signatures in the SP Cup 2016 dataset. During data preparation, recordings are sorted into audio and power groups based on inherent characteristics, further divided into 50 Hz and 60 Hz groups via spectrogram analysis. Residual blocks within the classification model extract frame-level embeddings, aiding decision-making through softmax activation. The topology and the hyperparameters of the shallow RawNet are optimized using a Neural Architecture Search. The overall accuracy of InterGridNet in the test recordings is 92%, indicating its effectiveness against the state-of-the-art methods tested in the SP Cup 2016. These findings underscore InterGridNet’s effectiveness in accurately classifying audio recordings from diverse power grids, advancing state-of-the-art geolocation estimation methods.
Pages: 16 to 21
Copyright: Copyright (c) IARIA, 2025
Publication date: March 9, 2025
Published in: conference
ISSN: 2519-8432
ISBN: 978-1-68558-245-6
Location: Lisbon, Portugal
Dates: from March 9, 2025 to March 13, 2025