mlsanityData to 7 Oct 2026

TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics

arXiv 2602.20317 ↗First posted 23 Feb 2026eess.SP cs.AI physics.plasm-phcited by 0

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Abstract (arXiv)

Next-generation fusion facilities like ITER face a "data deluge," generating petabytes of multi-diagnostic signals daily that challenge manual analysis. We present a "signals-first" self-supervised framework for the automated extraction of coherent and transient modes from high-noise time-frequency data across a variety of sensors. We also develop a general-purpose method and tool for extracting coherent, quasi-coherent, and transient modes for fluctuation measurements in tokamaks by employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII-D. Results are tested on data from DIII-D, TJ-II, and non-fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 62 references that are in the map. First posted on arXiv 23 Feb 2026.

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References · 11

  1. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  2. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  3. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 499
  4. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
  5. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  6. 2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
  7. 2023Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Networkcited by 12
  8. 2024Masked and Shuffled Blind Spot Denoising for Real-World Imagescited by 12
  9. 2020Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniquescited by 2
  10. 2025Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamakscited by 2
  11. 2021Self-Supervised Speech Denoising Using Only Noisy Audio Signalscited by 1

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