Are synthesised indoor noise signals using the sound reduction index of the façade sufficient to predict the annoyance of outdoor noise?
Bibliographic record
Abstract
Several studies have demonstrated the high level of annoyance caused by traffic noise to persons indoors. However, predicting how annoyed the resident are, is a quite complex and time-consuming task, especially if the scope includes the annoyance of different types of traffic noise through different facade elements. Ideally one would ask the residents within their homes how annoyed they are by the current traffic noise situation. But how do you ensure the residents are confronted with many different sources with different spectra. Common practice is to record the traffic noise indoors over a long period of time, cut the recordings into smaller segments, and play them back to the subject in a listening room or via headphones. This way the same signals can be presented to a larger set of subjects. These measurements would then need to be repeated at different locations. Therefore, the goal of this study is to investigate if indoor noise signals that are "filtered" using the sound reduction index of the façade give the same annoyance results as the actual recorded indoor noise signals. Thereby, making it only necessary to capture outdoor noise signals and simulating the transmission through a variety of different "artificial" facades.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".