PERIODIC DISTURBANCE CANCELLATION USING A GENERALIZED PHASE-LOCKED LOOP
Bibliographic record
Abstract
An effective technique for extracting an audio input from a composite signal that contains a nonstationary noise-corrupted periodic disturbance is presented. The proposed technique cancels the periodic disturbance using a synthesized signal whose parameters are adjusted adaptively. A combination of a generalized phase-locked loop (PLL) and an adaptive least mean square (LMS) method is used. The PLL creates a pair of basis signals that are phase-locked with the fundamental harmonic of the periodic disturbance. Harmonics generated from these basis signals are then used in the LMS method to minimize the average power of the residual nonperiodic signal. The virtue of this approach is that it does not depend on an explicit accurate estimate of the fundamental frequency of the disturbance. Furthermore, relatively large changes in the fundamental frequency can be tracked so long as they remain within the acquisition range of the PLL. Simulations and experimental results are presented that demonstrate the effectiveness of the proposed technique.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".