Optimal active noise control in large rooms using a “locally global” control strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it