Fly-over aircraft noise measurement campaign at Montreal-Trudeau airport using a microphone array
Bibliographic record
Abstract
Engines being quieter due to high by-pass ratios, the airframe noise, produced for instance by landing gears or high-lift devices, has become a significant contributor to the total noise radiated by aircraft during approach and landing. As part of the investigations carried out to understand noise generation mechanisms, the beamforming techniques developed over the last decade and applied to microphone array measurements have shown to be effective tools for localization and quantification of these aerodynamic noise sources. In order to validate their in-house beamforming softwares, Bombardier Aerospace and the Groupe d’Acoustique de l’Université de Sherbrooke have conducted a 5-day measurement campaign in June 2012. The 95-microphone array was located on the roof of a building next to the Montreal-Trudeau airport. Aircraft position was determined by two high-definition cameras, both synchronized with the microphone array by inter-range instrumentation group time codes generators. This paper summarizes the measurement campaign. The aircraft tracking tool and the beamforming algorithms used to characterize the noise sources are presented. Several Bombardier CRJ fly-overs were recorded during this test. Beamforming results obtained for different airlines are compared in order to evaluate the repeatability of the method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".