Microphone Array Applications in Cabins
Bibliographic record
Abstract
Air transportation has always been linked with aeroacoustic noise generation. To study these noise generating effects wind tunnels are used with scaled models because of the limited wind tunnel size. But studies of interior noise problems with scaled models are difficult as the model structure behaves differently in terms of sound transmission compared with an authentic cabin. Therefore, an in-situ measurement technique for source localization during operation is needed. In addition to the other noise-generating sources the aerodynamic and aeroacoustic excitation of the fuselage causes energy transmission into the cabin. The sound field generation is induced by structure-borne sound radiation of the lining (or window or floor). Due to the limited space in cabins and a vibrating lining which surrounds the measurement area, a pivotable array with 60 microphones was constructed. Because of the reverberation in cabins, sound source localization with conventional beamforming is not possible, so that an average beamforming method has to be used for sound source localization. A comparison between average and the conventional beamforming methods will be shown. First results from measurements in the DLR Do728 cabin test facility will demonstrate the source localization in cabins with this method. The average beamforming method is not limited to aircraft cabins and can be used for sound source localization in high-speed trains as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.022 |
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".