Fast Spectrogram Event Extraction via Offline Self-Supervised Learning: From Fusion Diagnostics to Bioacoustics
#self-supervised learning #spectrogram analysis #fusion energy #neural networks #signal processing #data deluge #real-time analysis #tokamaks
📌 Key Takeaways
- Researchers developed a self-supervised framework for automated extraction of coherent and transient modes from noisy data
- The framework achieves real-time processing with just 0.5 seconds inference latency
- Tested on data from fusion facilities DIII-D and TJ-II, plus non-fusion spectrograms
- Applications extend from fusion diagnostics to bioacoustics
- The tool addresses the 'data deluge' challenge from next-generation fusion facilities
📖 Full Retelling
Researchers led by Nathaniel Chen and eight colleagues from various institutions announced the development of a novel 'signals-first' self-supervised framework for automated extraction of coherent and transient modes from high-noise time-frequency data in a paper submitted to arXiv on February 23, 2026, addressing the 'data deluge' challenge faced by next-generation fusion facilities like ITER that generate petabytes of multi-diagnostic signals daily. The team created a general-purpose method employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate, tested on data from multiple fusion facilities including DIII-D and TJ-II, as well as non-fusion spectrograms. With an impressive inference latency of just 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control systems, representing a significant advancement in handling the massive amounts of complex data generated by fusion experiments. While initially developed for fusion diagnostics, the researchers highlight the broader applicability of their approach, noting its potential use in bioacoustics and other fields requiring analysis of time-frequency spectrograms with high noise levels.
🏷️ Themes
Artificial Intelligence, Signal Processing, Energy Research
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Original Source
--> Electrical Engineering and Systems Science > Signal Processing arXiv:2602.20317 [Submitted on 23 Feb 2026] Title: Fast Spectrogram Event Extraction via Offline Self-Supervised Learning: From Fusion Diagnostics to Bioacoustics Authors: Nathaniel Chen , Kouroche Bouchiat , Peter Steiner , Andrew Rothstein , David Smith , Max Austin , Mike van Zeeland , Azarakhsh Jalalvand , Egemen Kolemen View a PDF of the paper titled Fast Spectrogram Event Extraction via Offline Self-Supervised Learning: From Fusion Diagnostics to Bioacoustics, by Nathaniel Chen and 8 other authors View PDF HTML Abstract: 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. 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 this https URL . Subjects: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI); Plasma Physics (physics.plasm-ph) Cite as: arXiv:2602.20317 [eess.SP] (or arXiv:2602.20317v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2602.20317 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nathaniel Chen [ view email ] [v1] Mon, 23 Feb 2026 20:03:10 UTC (8,811 KB) Full-text li...
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