MétaCan
Menu
Back to cohort
Record W6998994356

Bridging the Gap Between Sound and Non-Sound Professionals with Virtual Reality

2023· article· en· W6998994356 on OpenAlexafffundvenue

Bibliographic record

VenueCanadian acoustics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
FundersCentre for Interdisciplinary Research in Music Media and Technology
KeywordsSoundscapeBridging (networking)Virtual realityRendering (computer graphics)Sound (geography)ScarcityWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.012
Scholarly communication0.0180.016
Open science0.0030.031
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0130.004

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.

Opus teacher head0.070
GPT teacher head0.382
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian acousticsSame topicNoise Effects and ManagementFrench-language works237,207