Multi-cultural perception of impact sound -- An international online listening survey about the perceived annoyance due to impact sounds
Bibliographic record
Abstract
To support the introduction of requirements for protection from impact noise in the National Building Code of Canada, the National Research Council of Canada implemented pilot subjective evaluations of impact sounds to evaluate the best metric to be used in the Code. As an alternative to the typical laboratory-based listening experiments, online-based listening tests were used. 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 was especially relevant in the context of the Covid-19 pandemic, which forced researchers to re-evaluate in-person procedures. This online listening survey was published for world-wide access, enabling data collection across a diverse target audience in many parts of the world. The survey and its preliminary results are presented and discussed in this paper. Data collected as part of the online survey, such as the person's country of residence and the type of dwelling they lived in, is used to explore the multicultural 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".