The spatial distribution of sound pressure within the human ear canal
Bibliographic record
Abstract
The sound field inside a human ear canal has been computed using 2 approaches. A simple model, a modified Webster horn equation approach, can accommodate the curvature and varying cross section of the ear canal. Calculations using the horn equation demonstrate the formation of standing waves within the canal. The pressure may be interpreted as either the pressure along the center axis of the canal or the average pressure within a cross-sectional slab. To investigate possible spatial variation through a cross section, the sound field has also been computed using the boundary element method (BEM). Over 2000 triangular mesh elements, 1 mm or less in size, were used to represent the canal geometry. For a plane piston source at the canal entrance and both a rigid and a resistive impedance condition at the eardrum position, the computed sound pressures along the center axis of the ear canal are in good agreement with the horn equation calculations, up to 15 kHz. The BEM approach, though, reveals spatial variations of sound pressure through each canal cross section, increasingly significant as frequency increases. Further, for source configurations that are more realistic than a simple piston, large transverse variations in sound pressure are anticipated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".