Uncertainty in tone quantification methods of background noise for enclosed spaces
Bibliographic record
Abstract
The noticeable tones in background noises can annoy and disturb human listeners. The noise community has been developing methods to quantify perception of tones and lately trying to propose new tone guidelines to regulate the maximum level of tones in noise. Prior to proposing guidelines, the reliability of existing tone quantification methods should be examined. Thus, this paper investigates uncertainty of the tone quantifying methods from ANSI or ISO standards including Tonal Audibility, Prominence Ratio, and Tone-to-Noise Ratio. This study will discuss major causes of uncertainty in measuring the tones from using the methods. It will cover definitions of tones in each method, how these metrics separate tones and broadband noises, and how they analyze signals in time and frequency domains. Lastly, this paper will also investigate effects of room modes on the measured tonality in indoor environments. The variances of the measured tonality across measurement positions for the assorted metrics will be presented. Acceptability of the variances in tonality will also be discussed.
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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 | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".