The influence of tympanic-membrane orientation on acoustic ear-canal quantities: A finite-element analysis
Bibliographic record
Abstract
Assuming plane waves, ear-canal acoustic quantities, collectively known as wideband acoustic immittance (WAI), are frequently used in research and in the clinic to assess the conductive status of the middle ear. Secondary applications include compensating for the ear-canal acoustics when delivering stimuli to the ear and measuring otoacoustic emissions. However, the ear canal is inherently non-uniform and terminated at an oblique angle by the conical-shaped tympanic membrane (TM), thus potentially confounding the ability of WAI quantities in characterizing the middle-ear status. This paper studies the isolated possible confounding effects of TM orientation and shape on characterizing the middle ear using WAI in human ears. That is, the non-uniform geometry of the ear canal is not considered except for that resulting from the TM orientation and shape. This is achieved using finite-element models of uniform ear canals terminated by both lumped-element and finite-element middle-ear models. In addition, the effects on stimulation and reverse-transmission quantities are investigated, including the physical significance of quantities seeking to approximate the sound pressure at the TM. The results show a relatively small effect of the TM orientation on WAI quantities, except for a distinct delay above 10 kHz, further affecting some stimulation and reverse-transmission quantities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".