Measurement of diffuse sound reflection from an impedance surfaces using one microphone by bayesian inversion.
Bibliographic record
Abstract
This paper discusses the use of inverse methods to find the absorption and diffusion characteristics of surfaces. An impedance surface in an anechoic chamber is excited by a pure tone source above it. The steady-state sound level is measured at n points above the impedance surface. The distribution of the n steady-state sound-pressure levels is assumed to be Gaussian. The set of mean or predicted values for this distribution is generated by finding the modulus of a modified Sommerfeld boundary element solution to the Helmholtz equation. The modification is to add multiple diffusely reflected waves each of which is additionally attenuated by a distribution that is proportional to sin(2θ)×G(θ)Dθ×H(φ)Dφ, where G(θ) is the piecewise function [G(θ)=θ/θSpec, θ⩽θSpec, and [(π/2)−θ]/[(π/2)−θ]Spec otherwise] and H(φ)=|(1−φ)/π|. The system of equations is then optimized for the specific impedance of the surface, the normal diffusion coefficient, and the azimuth diffusion coefficient (Z, Dθ, and Dφ) using Bayesian inversion. This process is repeated for two surfaces (painted plywood over 16 inch studs and painted plywood over 16 inch studs with randomly placed wooden blocks) at six frequencies (250, 500, 1000, 2000, 4000, and 8000 Hz).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".