Multi-variate error analysis of beam-forming acoustic measurements in a wind-tunnel
Bibliographic record
Abstract
Source localization has been in the forefront in acoustics, since quantification of the source power and its exact location is de rigueur for successful noise control. Starting from single microphone measurements, many different methods, progressively more successful and complex, have been attempted for source localization. Recently, beam-forming methods, due to their success in underwater acoustics and architectural acoustics, have been used in wind tunnel tests. These tests have been quite extensively utilized both in automotive and aircraft tests. Beamforming techniques use an array of microphones, arranged in different patterns, to detect the source location and source power. In beamforming, the array in effect beams, not in the physical sense, towards the source to determine its position. Various parameters determine the success of the beamforming techniques. Some of these parameters are: number of microphones, microphone spacing, array pattern, source frequency and signal analysis procedures. A series of microphone array tests were conducted at the national Research Council of Canada’s wind tunnel, with and without flow. The source location was known a priori. A multi-variate error analysis was performed to determine the importance of the different parameters. The results of the analysis will be presented in this paper.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".