Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

• URL: http://arxiv.org/abs/2409.01532v1
• Date: Tue, 3 Sep 2024 02:03:50 GMT
• Title: Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
• Authors: Joel Brogan, Olivera Kotevska, Anibely Torres, Sumit Jha, Mark Adams,
• Abstract summary: Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection. These methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks.
• Score: 4.259762400898358
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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