Comparison of an analytic horn equation approach and a boundary element method for the calculation of sound fields in the human ear canal
Bibliographic record
Abstract
The sound field inside a model human ear canal has been computed, to show both longitudinal variations along the canal length and transverse variations through cross-sectional slices. Two methods of computation were used. A modified horn equation approach parametrizes the sound field with a single coordinate, the position along a curved center axis-this approach can accommodate the curvature and varying cross-sectional area of the ear canal but cannot compute transverse variations of the sound field. A boundary element method (BEM) was also implemented to compute the full three-dimensional sound field. Over 2000 triangular mesh elements were used to represent the ear canal geometry. For a plane piston source at the entrance plane, the pressure along the curved center axis predicted by the two methods is in good agreement, for frequencies up to 15 kHz, for four different ear canals. The BEM approach, though, reveals spatial variations of sound pressure within each canal cross section. These variations are small below 4 kHz, but increase with frequency, reaching 1.5 dB at 8 kHz and 4.5 dB at 15 kHz. For source configurations that are more realistic than a simple piston, large transverse variations in sound pressure are anticipated in the vicinity of the source.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".