A Numerical Study of a Method for Estimating Sound Absorption Coefficient under a Synthesized Diffuse Acoustic Field
Bibliographic record
Abstract
A method for estimating the sound absorption coefficient of a material under a synthesized Diffuse Acoustic Field was recently proposed, as an alternative to classical sound absorption measurements in reverberant rooms (Robin O., Berry A., Doutres O., Atalla N., ‘Measurement of the absorption coefficient of absorbing materials under a synthesized diffuse acoustic field’, J. Acoust. Soc. Am., 136 (1) EL13-EL19, 2014). Using sound field reproduction approaches and a synthetic array of acoustic monopoles facing the material, estimation of the sound absorption coefficient under a reproduced Diffuse Acoustic Field in a hemi-anechoic room was shown to be feasible. The method was successfully tested on a few samples of melamine foam of close thicknesses and areas, but the influence of several parameters such as the source height, or the samples dimensions together with the nature of the porous material was not fully investigated. In this paper, the robustness of the method is numerically studied by examining the influence of the aforementioned parameters on the estimated sound absorption coefficient, with various square samples of homogeneous materials and a synthetic array of fixed dimensions. The principle of the method will be first recalled, and the main results of these numerical experiments will be then reported.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".