Phase coherence of spontaneous otoacoustic emissions derived from a cochlear model
Bibliographic record
Abstract
Spontaneous otoacoustic emissions (SOAEs) are commonly presented by averaging spectral magnitudes calculated in adjacent time segments of the recorded signal. To include the information carried by SOAE phase, Bergevin et al. [under preparation] proposed calculating the phase difference between adjacent time segments and then determine the vector strength to compute a measure of phase coherence. In stronger human SOAE peaks (magnitude >0 dB SPL), phase coherence plotted as a function of segment duration usually shows a maximum at around 100 ms, which shortens with increasing SOAE frequency above ∼2 kHz. Similar bell shaped dependencies coherence can be observed for simulated SOAEs from a nonlinear cochlear model composed of fluid-coupled oscillators, which simulate the transversal displacement of the basilar membrane (BM) [Vencovský et al. JASA, 2020]. The simulated BM displacement is amplified by feedback undamping force, which is transformed by a sigmoidal nonlinearity proportional to the second-order Boltzmann function. To evoke SOAEs, irregularities are added into the undamping feedback force and the model is driven by broad band noise applied into the eardrum (external noise) and into the undamping feedback force (internal noise). It appears that a combination of external and internal noise is needed to obtain the bell-shaped dependence of phase coherence on the segment duration similar to the one observed in human SOAEs. These results help guide how stochastic considerations must be included into active cochlear models.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".