A New Narrowband Active Noise Control System with Improved Applicability
Bibliographic record
Abstract
In conventional narrowband active noise control (NANC), the discrete Fourier coefficients (DFC) based linear combiner (LC) or the first-order FIR filter with two weights, also called magnitude and phase adjuster (MPA), is used as controller to make the system efficient. Modified NANC systems have been proposed to reduce the computational load of the conventional NANC. In this paper, we propose a NANC system that uses a relatively long FIR filter rather than the two-weight MPAs and only includes a single reference signal filtering (x filtering) block. The reference cosine waves are added up to form a new reference signal that is fed to not only the FIR controller but also the x -filtering block. Comparisons are made in detail between the existing modified MPA-based NANC system and the proposed system in terms of computational cost and noise reduction performance (NRP). Extensive simulations have been conducted to reveal that the proposed NANC system can not only outperform the modified MPA-based NANC system but also requires less computational cost if the number of target frequencies is larger than a specific value.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".