Interpeak characterizations for spontaneous otoacoustic emissions
Bibliographic record
Abstract
One manifestation of the "active ear" is the presence of spontaneous otoacoustic emission (SOAE), which also exhibit salient connections to perception such as threshold microstructure.Historically, SOAE modeling efforts initially focused upon a single limit-cycle oscillator.However, SOAE spectra from a given ear typically exhibit multiple peaks, and more current models consider a spatially distributed tonotopic system with various types of coupling.SOAEs have nonstationary properties (e.g., amplitude and frequency modulations), which may be crucially tied to the coupling of active elements in the ear.Thus, to better biophysically constrain models, this study seeks to improve characterization of general non-stationary features of SOAE peaks as well as interrelations of such between them.Given the ubiquitous nature of SOAE across the animal kingdom, we analyze SOAE waveforms from a variety of species exhibiting disparate inner ear morphologies (e.g., human, barn owls, Anolis lizards).This manuscript provides a preliminary account of our analyses and focuses on the Anolis lizard.Upon filtering in the spectral domain, we characterize temporal properties of individual peaks, including possible amplitude-modulation (AM) and frequency-modulation (FM).Further, we perform correlation analyses of such between peaks to determine types of interactions and how such might vary across time.Initial results are consistent with previous reports (e.g., [1,2]) in that an SOAE interpeak correlations for a given ear are idiosyncratic: Sometimes peaks (adjacent or not) exhibit correlated (positive or negative) AM and/or FM fluctuations with delays up to the order of milliseconds (typically longer for humans, shorter for lizards), while sometimes no correlation is observed.We attempt to frame these results within the broader context of specific SOAE modeling approaches.A common feature of the healthy ear across the animal kingdom is the generation of spontaneous otoacoustic emission (SOAE).This phenomenon is often described as a by-product of an underlying active mechanism that metabolically boosts the sensitivity and selectivity of the ear.While many theories have been proposed (e.g., [3,4,5,6]), SOAE generation remains relatively poorly understood, especially when considering gross inner ear morphological differences across the animal kingdom [7].Several key characteristics of SOAE are commonly observed.First, not all ears emit.A healthy individual can have normal hearing but exhibit no SOAE.Further, the presence of SOAE is common but not universal across the animal kingdom: While relatively robust in primates and numerous lizard species, SOAE activity is conspicuously absent in many animals commonly used in auditory neurophysiology.For example, SOAE in mice are exceedingly rare unless mutations to their tectorial membrane are present [8].Second, SOAE activity is mostly confined to the most sensitive portion of the audiogram.Third is the general characteristic that SOAE commonly manifest as peaks of variable width in the spectrum of the measured microphone signal (hence "SOAEs" from an ear).This narrowband feature has provided a focal point for many facets of SOAE analysis and theory, despite the fact SOAE activity can also be present in a broadband fashion (e.g., the "baseline" activity in geckos [9] and skinks).Fourth, these peaks can (but not always) exhibit statistical properties consistent with self-sustained sinusoids [10,11,12].Lastly, SOAE activity appears to readily interact with external acoustic stimuli, allowing for measurements such as "suppression tuning curves" that can exhibit selectivity similar to that of single auditory nerve fibers (e.g., [13]).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".