Noise in your web browser: An online listening study regarding the perceived annoyance due to impact sounds
Bibliographic record
Abstract
Laboratory listening studies are typically limited in the number of people who can participate because of the effort that is involved in setting up and carrying out the experiment for each participant. This effort has significantly increased since the start of the COVID-19 pandemic due to the heightened public health requirements for in-person studies. This increase in effort and the resulting limit on the number of participants can be avoided by implementing the listening study as an online interface, where participants can then run the experiment from the comfort of their home and anyone who has access to a computer and headphones is able to participate. Such an approach supports the goal of involving the general public, who are the ultimate target audience for the research outcome. This contribution presents the preliminary results of an online listening study assessing the perceived annoyance due to impact sounds in residential buildings. The interface was previously validated and presented (Internoise 2021) with limited data. Since then, the survey has been published online for worldwide access. The results are discussed in relation to the results of previous laboratory studies.
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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 | 0.002 | 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 | 0.001 |
| 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".