A Hybrid Signal Processing Framework for the Acoustic Noise Monitoring of Offshore Wind Turbines
Bibliographic record
Abstract
This paper presents a novel hybrid signal processing framework to address the challenges posed by the non-stationary and complex noise environments characteristic of offshore wind turbines. The proposed framework integrates adaptive noise transformation, predictive filtering, and customized time-frequency decomposition techniques to enhance signal detection and reconstruction fidelity. A key novelty is the noise clustering and resampling mechanism that reshapes irregular noise distributions using a modified k-means algorithm with robust statistical methods to differentiate between environmental (e.g., wave and wind-induced) and mechanical noise (e.g., gearbox vibrations, blade resonances) while preserving critical features relevant for detection. The framework incorporates adaptive state-space models with dynamic feedback calibration employing an entropy-based cost function, which adjusts parameters in real time based on changing noise conditions. In addition, a custom-designed wavelet decomposition featuring frequency-adaptive Gaussian kernels is introduced to enhance transient noise discrimination beyond standard wavelet transforms. Extensive experimental validation with real-world underwater acoustic signals including diverse noise profiles and varying signal-to-noise ratios demonstrates significant reductions in false alarms, enhanced detection sensitivity, and scalable computational performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".