Bridging the Gap Between Sound and Non-Sound Professionals with Virtual Reality
Bibliographic record
Abstract
Urban soundscape planning remains a challenge to many due in part to a scarcity of soundscape design tools. While many sound planning tools exist, they are generally geared towards acousticians rather than non-sound-based Professionals of the Built Environment (PBEs) (e.g., urban designers, planners, or landscape architects). This disconnect produces pressure points for both PBEs and acoustics experts, resulting in sound being considered late in urban projects and large burdens being placed on acousticians to consider many aspects outside their own areas of expertise. This work presents a new 3D virtual reality simulator to help PBEs consider sound in outdoor public spaces. City Ditty was created through a user-centered design process, focused on identifying and evaluating functionalities that would benefit PBEs that do not have much experience with sound. Through focusing on the auditory experience via a soundscape framework, this can help people learn to talk about sound in an accessible way and give them simple tools to consider different sound intervention strategies. This was done through a self-guided sound-awareness session that walks the user through 36 tasks in desktop virtual reality. These hands-on tasks both illustrate soundscape 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. Early testing indicated that users could use this to 1) learn how to use the software itself, 2) learn basics of soundscape design, and 3) implement their own simple soundscapes in less than an hour. This presentation gives an overview of current barriers and possible timelines for adoption, followed by preliminary results of a new usability study that extends this work into head-mounted virtual reality with enhanced audio capabilities.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".