Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

2024/09/03 by Joel Brogan, Olivera Kotevska, Brogan, Joel +7
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2409.01532

openalex publication_date 2024/09/03 · openalex created_date 2024/09/29 · openalex updated_date 2026/08/01

Abstract

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring.

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