Bringing the Coastline to the City: Laboratory Evaluation of Urban Soundscapes in the Presence of Ambient Sound art
Bibliographic record
Abstract
Sound art in public spaces can shape visitors’ experience, yet few studies have explored the impact of curated sound installations on soundscape. We report on the evaluation of the soundscape of a small urban public space in Montreal, Canada in the presence of different compositions designed for a sound installation, Les Madelinéennes by Charles Montambault. Residents familiar with the site (N = 25) evaluated various soundscapes in laboratory settings, combining Ambisonics field recordings of the site with spatialized simulations of compositions featuring coastal sounds. Participants rated the resulting soundscapes along semantic scales, identified significant moments, and described them in follow-up interviews. Results revealed that more evocative sounds (boat horns, seagulls, cormorants) were perceived as less appropriate and pleasant, while less evocative sounds (wind, sparrow) were more pleasant and soothing. Interviews also revealed a diversity of associations to the added sounds. The study was well received by the local community and led to design recommendations for the installation on site.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".