Preliminary study on active control of double-glazing with pressure compensation using an in-cavity microphone
Bibliographic record
Abstract
Enhancing the sound insulation in double-glazed units involves actively controlling inter-glazing cavity pressure to decouple each glass. The optimal strategy to maximize transmission loss requires loudspeakers in the cavity to generate anti-noise and error microphones in the reception room, outside of the cavity. However, this configuration is not suitable for a standalone product where out-of-cavity microphones are not allowed. This study presents an original method to experimentally learn the control of one loudspeaker with one error microphone, both inside the cavity, in order to maximize acoustic isolation in a low-frequency band. The method requires a compensation FIR filter, experimentally identified during a learning phase when an optimal command is applied. Experiments were conducted using an active symmetrical double-glazed unit (30.4 x 30.6 cm, 6 mm thick, 60 mm apart cavities) in a "waveguide" setup. Results show an average transmission loss gain from 50 to 550 Hz of 10 dB with in-cavity microphone compensation. It nearly reaches optimal performance, 11 dB reduction in transmitted waves, compared to 7 dB for the uncompensated in-cavity microphone. This encouraging preliminary result leads to further developments to extend the method to multiple loudspeakers and multiple microphones.
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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 | 0.001 |
| 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 | 0.000 |
| 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".