What is realistic enough? Design considerations for soundscape simulators
Bibliographic record
Abstract
Urban soundscapes reflect life and behaviours in a city. Given the dynamic and ever-changing behaviours of people, this introduces many complexities for soundscape simulators. We focus on bottom-up simulators, wherein every aspect of an environment can potentially be simulated, including 3D models of spaces, people’s behaviours, sound sources, weather, nature, lighting, and the physics behind them to create the illusion of navigating a real space. How much realism is necessary in such virtual environments though? This work discusses realism through the lenses of plausibility, hyperrealism, and ecological validity. Is it ok to just be a plausible reality? Should reality be exaggerated? How faithful should it be and from which perspective (e.g. matching physical or cognitive “realities”)? These questions were considered during the development of our in-house soundscape simulator, City Ditty. City Ditty seeks to be operable by non-sound professionals and support integration into urban projects with minimal expertise and resources, thus encouraging more diverse urban professionals and city users to contribute to how their cities will sound through participatory approaches. Given these requirements, we discuss how suitable levels of realism can be attained to fit people’s needs at technical, practical, and theoretical levels by considering these three lenses for design.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".