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Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations (2409.01532v1)
Published 3 Sep 2024 in cs.LG, cs.AI, and cs.CV
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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