A novel method for objective in-situ measurement of audibility in bone conduction hearing devices – a pilot study using a skin drive BCD
Bibliographic record
Abstract
OBJECTIVE: Objective measurement of audibility (verification) using bone conduction devices (BCDs) has long remained an elusive problem for BCDs. For air conduction hearing aids there are well-defined and often used objective methods, and the aim of this study is to develop an objective method for BCDs. DESIGN: In a novel setup for audibility measurements of bone-anchored hearing aid (BAHA) attached via a soft band, we used a skin microphone (SM) on the forehead measuring in-situ sound field thresholds, maximum power output (MPO) and international speech test signal (ISTS) responses. STUDY SAMPLE: Five normal-hearing persons. RESULT: Using the electrical output of SM it was possible to objectively measure the audibility of a skin drive BCD, presented as an eSPL-o-gram showing thresholds, MPO and ISTS response. Normalised eSPL-o-gram was verified against corresponding FL-o-grams (corresponding force levels from skull simulator and artificial mastoid (AM)). CONCLUSION: The proposed method with the SM can be used for objective measurements of the audibility of any BCDs based on thresholds, MPO and speech response allowing for direct comparisons of hearing and BCD output on the same graph using an eSPL-o-gram. After normalisation to hearing thresholds, the audibility can be assessed without the need for complicated calibration procedures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".