One size does not fit all: Three tools and approaches for soundscape simulations
Bibliographic record
Abstract
We present a reflection on three prototypes of real-time interactive soundscape simulators aimed at supporting participatory urban sound planning and interventions. These prototypes were developed as part of the Sounds in the City cross-sectoral partnership through an iterative process involving various stakeholders. Each prototype enables different ways to manipulate soundscapes through tailored interfaces and audio/visual outputs, targeting different types of users. The first version is audio-only and uses live music tools for Ambisonics spatialization, with limited environmental modeling for co-design exercises with urban and sound professionals. The second version builds on the idea of the first and adds acoustic modeling. It was used to assess the impact of sound installations in public spaces through research-creation involving sound artists and residents. The last version utilizes (desktop or head-mounted) virtual reality with binaural rendering, immersing the user in an audio-visual city to raise sound awareness and support urban soundscape design. We emphasize that there is no one-size-fits-all tool. Rather, we highlight how different tools are needed for different auralization tasks and target user groups. These tools are presented through examples of early-stage conceptualization, educational components, creative processes, laboratory-based soundscape assessments, and both individual and participatory design sessions.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".