Subjective studies on impact sound in times of a pandemic -- a comparison between a laboratory study and an online listening test
Bibliographic record
Abstract
The National Research Council Canada is currently investigating the perceived annoyance due to impact sound in multi-unit residential buildings (MURBs). The first part of a subjective laboratory study on a number of different floor/ceiling assemblies was completed with 26 participants just before the start of the Covid-19 pandemic in 2020. To evaluate the feasibility of carrying out a similar study without in-person attendance, the same stimuli from the laboratory study were used to create an online listening test. The online listening test was created in JavaScript and HTML5 to run on any internet browser. This paper will present the results of the online listening test and compare them to the laboratory study, focusing on the obvious drawbacks of an uncontrolled remote study such as the uncertainty due to the participants' headphones and listening environment. With an expectation that in-person studies will remain difficult to realize in the near future, this contribution provides evidence whether remote subjective listening tests are a viable alternative to controlled laboratory studies for impact sound.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".