Time domain nearfield acoustical holography without wrap-around error and spectral leakage for forward propagation
Bibliographic record
Abstract
Nearfield acoustical holography can be used to visualize time domain evolution of non-stationary sound fields. It requires sampling of the pressure field with a microphone array to calculate the sound field on other parallel surfaces, either toward the source or farther away from it. This paper focuses on forward propagation of fields sampled with a planar array. Several formulations exist to perform such calculation; however, despite being mathematically well-posed, they generate significant errors, especially for larger propagation distances. These errors are mainly due to wrap-around and spectral leakage. It is shown in this paper that generation of leakage can be avoided by sampling the analytical Green's function in the time and space domains. Such formulation allows the application of linear convolution, which completely suppresses wrap-around errors. The results obtained from the proposed formulation are compared to the analytical solution of a baffled piston with transient displacement. The average discrepancy is below 3.5% for propagation distances of 5 to 55 cm. In comparison, the relative error obtained with other propagators varies from 6% to 73%, depending on the propagation distance and method used. Finally, the proposed formulation is applied to experimental data obtained from an impacted plate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".