Evaluating signal-to-noise ratios, loudness, and related measures as indicators of airborne sound insulation
Bibliographic record
Abstract
Subjective ratings of the audibility, annoyance, and loudness of music and speech sounds transmitted through 20 different simulated walls were used to identify better single number ratings of airborne sound insulation. The first part of this research considered standard measures such as the sound transmission class the weighted sound reduction index (R(w)) and variations of these measures [H. K. Park and J. S. Bradley, J. Acoust. Soc. Am. 126, 208-219 (2009)]. This paper considers a number of other measures including signal-to-noise ratios related to the intelligibility of speech and measures related to the loudness of sounds. An exploration of the importance of the included frequencies showed that the optimum ranges of included frequencies were different for speech and music sounds. Measures related to speech intelligibility were useful indicators of responses to speech sounds but were not as successful for music sounds. A-weighted level differences, signal-to-noise ratios and an A-weighted sound transmission loss measure were good predictors of responses when the included frequencies were optimized for each type of sound. The addition of new spectrum adaptation terms to R(w) values were found to be the most practical approach for achieving more accurate predictions of subjective ratings of transmitted speech and music sounds.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".