Sound perception study of an eVTOL vehicle
Bibliographic record
Abstract
The need for sustainable and quiet air transport, combined with advances in electric and battery technology, makes the novel Urban Air Mobility (UAM) concept a reality. The development of Eve Air Mobility's Electrical Vertical Take-Off and Landing (eVTOL) vehicle enables quick passenger transport in overcrowded or congested areas. Public acceptability and experience are essential for the successful introduction of these new modes of transport. Therefore, a perception study was conducted in three USA cities to measure people's responses and compare visual and sound events with other modes of air transport such as helicopters and turboprop aircraft. Early design parameters of the eVTOL were used to auralize the sound of Eve's aircraft during take-off and flyover operations. eVTOL sounds and recordings from other vehicles were mixed with real environment sound and visual recordings into a Virtual Reality-based simulator. Subsequently, over 110 people in three cities participated in a jury study. The results showed that simulated eVTOL flyovers are perceived as quieter and less annoying than similar helicopter operations. This study will help inform communities on how to best benefit from UAM while minimizing any negative effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".