City Ditty: An Immersive Soundscape Sketchpad for Professionals of the Built Environment
Bibliographic record
Abstract
Soundscape planning remains a challenge to many urban practitioners due in part to a scarcity of soundscape design tools. While many sound planning tools exist, they are generally geared towards acousticians rather than professionals of the built environment (e.g., urban designers, planners, or landscape architects). This paper walks through the user-centered design process for the development and evaluation of a new soundscape design tool, City Ditty. A User-Centered Design approach was utilized to identify and develop functionalities that would benefit urban practitioners that do not currently specialize in sound. This began with a literature review of existing soundscape tools, followed by a user needs assessment with professionals of the built environment, consisting of a workshop including focus groups, tech demos, and a collaborative soundscape design exercise. These results funneled into the development of City Ditty: an immersive soundscape sketchpad that facilitates rapid audio-visual prototyping of urban soundscapes. To make City Ditty accessible to users with no expertise in sound, we developed a sound awareness session that walks the user through 36 tasks. These hands-on tasks illustrate soundscape design principles while serving as instructions on how to use the many functions of City Ditty, e.g., listen to the city soundscape at different times of the day, pedestrianize the city centre, modify permissible construction times, and add birdfeeders to attract sounds of nature. A usability study was conducted with six participants to evaluate the tool using Desktop Virtual Reality, determine new functionalities, and determine how to best facilitate user engagement in order to encourage adoption by practitioners. The direction of future soundscape tools is discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".