Characterization of acoustic materials at arbitrary incidence angle using sound field synthesis
Bibliographic record
Abstract
Standardized methods for measuring sound absorption such as the impedance tube and reverberation chamber methods are limited to normal or diffuse incidence, respectively. Two research axes have been generally followed in the literature to develop alternative techniques, the first one focusing on the measurement part, that is from the two-microphone technique to the use of microphone arrays or pressure-velocity sensors. The second axis focuses on the excitation part with for instance the use of sound field synthesis techniques. Since acoustic impedance and sound absorption coefficient of materials are classically defined under normal and oblique plane wave excitation, synthesizing an “ideal” plane wave using a loudspeaker array would allow measuring these acoustics quantities using a simple microphone pair. In this article, the effect of the different parameters of a loudspeaker array on acoustic plane waves reproduction on a material’s surface is first numerically studied. Then, numerical and experimental results for the estimation of both impedance and absorption coefficients are reported. These results show that sound field synthesis allows to characterize a material for arbitrary incidence angles over a wide frequency range, thus offering an alternative method to standard techniques and an improvement over existing works.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".