A New Hybrid Active Noise Control System With Input-Power-Controlled Online Secondary-Path Modeling
Bibliographic record
Abstract
A new hybrid active noise control (ANC, HANC) system is proposed in this paper that is equipped with an input-power-controlled online secondary-path modeling (OSPM) subsystem. An FIR linear prediction filter (LPF) is newly included that takes the FIR supporting filter (SF) error as its desired signal and separates the remaining target narrowband noise component from all the other broadband noise components. Placed right after the LPF is the OSPM subsystem. The SF and LPF output signals, namely the target broadband and narrowband components that remain in the residual error are not only used to update the feedforward and feedback ANC (FFANC, FBANC) subcontroller, respectively, but are also adopted to control the power of the OSPM-exclusive auxiliary white Gaussian noise (AWGN) to pursue a trade-off between the OSPM quality and the AWGN contribution to the residual error. The OSPM error is utilized to simultaneously update not only the OSPM subsystem but also the SF and the LPF. Due to inclusion of the LPF, the adverse coupling effects among the FFANC, the FBANC and the OSPM is reduced substantially, leaving a possibility for improving the HANC overall convergence and noise reduction performance (NRP). Furthermore, preliminary steady-state analysis of the LPF is also conducted to reveal its properties and effectiveness. Extensive simulations with both synthetical and real settings are provided and conducted to verify that the proposed HANC system is superior to existing solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".