Evaluating airborne sound insulation in terms of speech intelligibility
Bibliographic record
Abstract
This paper reports on an evaluation of ratings of the sound insulation of simulated walls in terms of the intelligibility of speech transmitted through the walls. Subjects listened to speech modified to simulate transmission through 20 different walls with a wide range of sound insulation ratings, with constant ambient noise. The subjects' mean speech intelligibility scores were compared with various physical measures to test the success of the measures as sound insulation ratings. The standard Sound Transmission Class (STC) and Weighted Sound Reduction Index ratings were only moderately successful predictors of intelligibility scores, and eliminating the 8 dB rule from STC led to very modest improvements. Various previously established speech intelligibility measures (e.g., Articulation Index or Speech Intelligibility Index) and measures derived from them, such as the Articulation Class, were all relatively strongly related to speech intelligibility scores. In general, measures that involved arithmetic averages or summations of decibel values over frequency bands important for speech were most strongly related to intelligibility scores. The two most accurate predictors of the intelligibility of transmitted speech were an arithmetic average transmission loss over the frequencies from 200 to 2.5 kHz and the addition of a new spectrum weighting term to R(w) that included frequencies from 400 to 2.5 kHz.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".