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Record W4415455725 · doi:10.3397/in_2025_1074734

Sound perception study of an eVTOL vehicle

2025· article· en· W4415455725 on OpenAlexaff
Roalt Aalmoes, Naomi Sieben, Thaynan A. Oliveira, Wouter de Haan

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsSound (geography)QUIETPerceptionAircraft noiseSoundscapePublic transport

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.246
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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