Exploring the use of soundscape sketchpads with professionals
Bibliographic record
Abstract
Most urban professionals (planners, designers, policy-makers) are not trained in acoustics or soundscapes. However, the decisions that they make often shape the sound environments as sound touches on many aspects on urban life, including mobility, tourism and economic development. To better equip them to design with sound in mind, we have developed a new virtual-reality soundscape sketchpad, City Ditty, along with a short training session. A usability study revealed that users could learn basic soundscape principles and apply them to design their soundscapes in less than an hour. Such tools are not meant to replace acoustic software, but rather complement them by providing a simple interface to sketch audio/visual soundscapes, allowing people to experience the implications of their design decisions (e.g. pedestrianization, construction sites) across different contexts such as time of day, and season. Such sketches can act as discussion points for public consultations and help communicate requests to sound experts for further refinement. This paper extends existing work by further investigating how professionals see themselves integrating soundscape sketchpads into their work. Which stage(s) of their projects would befit such software? How can we support collaborative designs? When is head-mounted display vs. monitors appropriate? Our new study reports on these.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".