A simple low-invasive method to assess sound pressure levels at the eardrum using dual-microphone measurements in the open or occluded ear
Bibliographic record
Abstract
The assessment of the noise exposure for a given individual is commonly performed using measurement techniques such as sound level meters (SLM) combined with estimated of exposure time or the use of portable noise dosimeters (PND). SLM and PND-based approaches only provide information about the ambient noise levels and fail to account for wearer’s placement effects and inter-individual differences in the wearers’ morphologies (e.g. head and ear geometries). While the damage risk criteria of existing noise standards refer to free-field measurements, it is commonly accepted that the risk of hearing loss is more directly related to the levels at the tympanic membrane. In-ear noise dosimetry (IEND) is a promising approach that provides continuous monitoring of an individual's noise exposure directly inside the ear. However, current IEND systems do not allow direct collection of eardrum data, as their featuring in-ear microphone is typically maintained at a certain distance from the membrane. This paper presents a simple method aimed at converting the measured SPLs to the eardrum, thus forming the basis for individual in-situ calibration of IEND. The method, based on a dual-microphone approach, and prototypes developed to conduct improved IEND measurements in the open or occluded ear are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".