Aeroacoustics of drones
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4524.vid Experiments in the anechoic chamber at Université de Sherbrooke have been conducted to create a database of acoustic measurements on seven small quadrotor drones. These measurements have been achieved in both stationary and accelerating/decelerating flights with and without dropping objects to identify both noise signatures and sources in different operating conditions. In stationary flight, all drones show a dominant tonal noise signature (20~dB above the broadband noise levels) and an overall compact dipole directivity. No significant differences have been observed in other flight regimes. The more complex directivity of the dominannt blade passing frequency can also be modelled successfully by a decomposition in spherical harmonics. A catalogue of drone noise signature has then been built. A noise identification test of drones in various configurations suggests that the sound signature of a single microphone can discriminate efficiently the flight mode
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".