Effect of Acoustic Treatment and Table Dividers on Diners’ Experience in a Montréal Restaurant
Bibliographic record
Abstract
This study investigates the effect of acoustic treatment in an upscale restaurant in Montreal (at Institut du Tourisme et Hôtellerie du Québec). A questionnaire was administered to a total of 225 diners before (N = 140) and after (N = 85) the installation of acoustic panels on the ceiling of the dining room. Participants were asked to rate their overall experience, the soundscape of the restaurant, as well as their vocal and listening effort. Additionally, as this work was conducted during the COVID-19 pandemic, we explored the influence of transparent acrylic dividers between tables. Two-way MANOVAs were conducted to investigate the effect of acoustic treatment (before/after) and dividers (with/without). We observed significant effects of both interventions on vocal and listening effort, as well as a significant effect of acoustic treatment on satisfaction and soundscape judgments. Specifically, the presence of acoustic treatment improved overall satisfaction and soundscape judgments (rated as more pleasant and calmer) while reducing perceived vocal and listening effort, but without affecting the visual experience. On the other hand, the presence of table dividers resulted in worse soundscape judgments and increased vocal and listening effort. On theoretical grounds, the results highlight the potential of acoustic treatment to enhance diners’ experience as well as the detrimental effect of table dividers on acoustic comfort. On methodological grounds, the proposed questionnaire could be used to assess acoustic interventions from the user perspective in a wide range of settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".