Evaluating the performances of direct-to-consumer hearing devices: A comparative study of electroacoustic, acoustic transparency, and passive attenuation characteristics
Bibliographic record
Abstract
Direct-to-consumer (DTC) hearing devices, including personal sound amplification products, hearables, and over-the-counter hearing aids, offer the potential to improve communication and protect hearing in noisy environments. This study aimed to measure the electroacoustic, acoustic transparency, and attenuating performances of 11 DTC hearing devices. First, electroacoustic characteristics were evaluated following the guidelines of the American National Standards Institute/Consumer Technology Association [ANSI/CTA-2051, 2017, Personal Sound Amplification Performance Criteria (American National Standards Institute and Consumer Technology Association, New York)] standard. Then, acoustic transparency and passive attenuation measurements were collected on an acoustic test fixture by comparing recorded data from non-instrumented and instrumented ears. Based on these measurements, five metrics were developed to encompass different aspects of transparency and attenuation features, then they were computed. Most of the devices investigated met the requirements of the ANSI/CTA-2051 (2017) standard. Regarding transparency and attenuation features, the trends identified in this study suggest that although many devices perform well in specific areas, trade-offs exist that may affect their overall utility depending on the user's needs. Our findings highlight the importance of educating consumers about the performance and safety of DTC hearing devices and helping them choose the appropriate technologies that align with their hearing profiles and needs. The proposed metrics could allow for a more standardized comparison of the acoustic transparency and sound attenuation performances of DTC hearing devices.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".