Optimal active noise control in large rooms using a “locally global” control strategy
Bibliographic record
Abstract
The feasibility of applying active noise control in large rooms, in which global control is very difficult, is investigated. Local control can only ensure sound attenuation near error-sensor positions. Considering that workers usually work only in certain regions of a workroom, a new “locally global” control strategy is proposed. The objective is to reduce the acoustic potential energy in the target region. Compared to local control, “locally global” control ensures overall noise reduction over the target region. Compared to global control, it allows the number of control channels to be significantly reduced. The placements of the control loudspeakers and error microphones must be optimized to ensure that, while the sum of the squared sound pressures at the error sensors is minimized, the potential energy in the target region is reduced. Room sound fields are modeled using the image-source method and point sources. Genetic algorithms are used to optimize the locations of the control loudspeakers and error microphones. Both numerical and experimental results are presented. The sensitivities of control performance to variations in the excitation frequency, the control-source positions, and the error-sensor locations are investigated.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".