Impact sound around the world - An online listening survey about the perceived annoyance due to impact sounds
Bibliographic record
Abstract
To support a potential introduction of an impact sound requirement into the National Building Code of Canada, the National Research Council of Canada has initiated several research projects.One of these projects consisted of several laboratory listening experiments regarding the perceived annoyance due to impact sounds.As an alternative to the typical laboratory-based listening experiments, an online-based listening survey was published for world-wide access, from November 2022 to March 2023, enabling data collection across a diverse target audience in many parts of the world.The ability to collect data with an online survey allows to reach the general public much more than with any laboratory-based experiment, and it is especially relevant in the context of the Covid-19 pandemic, which has forced researchers to re-evaluate in-person procedures.In this paper, the online listening survey is presented and the results are discussed in relationship to the results of the laboratory tests that were carried out in Canada, Korea and Germany.Additional data that is collected in the online survey, such as the country of residence and type of housing, is used to explore the moderating effects on the annoyance ratings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".