The effect of sound masking on employees' acoustic comfort and performance in open-plan offices in Canada
Bibliographic record
Abstract
Sound masking systems are commonly used in open-plan offices to generate a controlled minimum level of background sound, in order to decrease the signal-to-noise ratio of intrusive speech and blend out transient office noise. However, a question in the acoustical design of offices is whether the self-generated noise of occupants may alone be sufficient to provide the background sound level conditions necessary to achieve similar levels of speech privacy and acoustic comfort as sound masking systems. This study examines the relationship between occupant-perceived speech privacy and acoustic comfort under three different acoustic scenarios (no masking, controlled 42 dBA, and 47 dBA masking sound levels). The study was conducted pre-COVID-19 in two separate open-plan offices located in Quebec, Canada that at the time were close to full occupancy. Employees completed subjective questionnaires before and after each change in conditions, focusing on how the sound environment impacted their comfort and work performance during the study. Statistical results show that the occupants were significantly more satisfied during the two sound masking conditions in comparison to the no-masking condition, where only the occupant-generated and exterior/mechanical system noise was present as the background sound. Implications for open-plan offices with lower occupancy conditions post-COVID-19 are 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".