Validation of a sound intensity imaging system for wall ISTC calculation, with leak detection
Bibliographic record
Abstract
This paper presents a comparison study of two methods that can be used for calculating the sound transmission classification (STC) of a sample wall in a test laboratory, as per ASTM E2249-2016. In both methods, the average sound pressure level within a reverberant room is taken, and the incident sound power on the wall in the source room is calculated. In the 1st method, the traditional method of E2249, an intensity probe is swept at a constant speed, across a set path, through a wire-frame grid specially constructed for the measurement. The sound power radiating from the whole wall is then calculated from the sound intensity, and the sound transmission loss is calculated by subtracting the radiated sound power of the wall from the incident sound power. Having calculated the TL, the STC can then be calculated via ASTM E413-2016. In the 2nd method, the sound intensity probe of the I-track sound intensity imaging system is swept across the wall surface at both an irregular speed and an irregular pattern. The sound power is instantly calculated by the software, as optical tracking of the probe means the spatial and temporal sampling of the sound intensity is made automatically. The sound power is then used to calculate the STC in the same manner as the traditional method.The paper presents the precision of the two methods (traditional vs imagery). Additionally, the paper will present a secondary benefit from the use of the imagery method not available to the traditional scan method; the ability to locate and rank acoustical weaknesses within the single surface scan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".