Experiential design tools for acousticsi—A retrospective and look ahead at the use of sound and visualizations for transportation noise
Bibliographic record
Abstract
Originally conceived and developed to inform the design of some of the world’s best arts and culture venues, over the past 10+ years, Arup SoundLab has also been used to create sound demonstrations that simulate and gauge response to environmental sound. Sound demonstrations combine aural and visual simulations to enable clients, designers, major stakeholders and the general public to experience and better understand sound. They provide robust objective information to support decision making and help shape better outcomes for all. The SoundLab has been used to inform the design of vertiport infrastructure; to assess annoyance and possible health impacts of novel noise sources; to inform local and international policy on noise; and to provide information on the early prototyping of Advanced Air Mobility (AAM) vehicles. These applications will be described in this presentation, including recent simulations for the Los Angeles Department of Transportation (LADOT) and a human response study for the European Union Aviation Safety Agency (EASA) to provide insight into people’s response to AAM noise impacts. As AAM applications broaden, auralisation and visualisation processes are being developed to facilitate understanding of planning, permitting and design processes. The immersive experience provides information that is valuable to the various parties involved.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".