Physiological assessment of contrast-enhancing frequency shaping and multiband compression in hearing aids
Bibliographic record
Abstract
Spectral enhancement is now being used in many hearing aids in an attempt to compensate for broadened cochlear filtering. However, spectral enhancement may be counteracted by multiband-compression algorithms designed to compensate for the reduced dynamic range of the impaired cochlea. An alternative scheme for spectral enhancement, contrast-enhancing frequency shaping (CEFS), has been proposed, which results in an improved neural representation of the first and second formants of voiced speech segments in the impaired ear. In this paper, models of the normal and impaired ear are used to assess the compatibility of CEFS with multiband compression. Model auditory nerve responses were assessed under four conditions: (1) unmodified speech presented to a normal ear; (2) amplified, unshaped speech presented to an impaired ear; (3) CEFS speech presented to an impaired ear; and (4) CEFS+multiband-compression speech presented to an impaired ear. The results show that multiband compression does not reduce the benefits of CEFS, and in some cases multiband compression assists in preventing distortion of the neural representation of formants. These results indicate that the combination of contrast-enhancing frequency shaping and multiband compression should lead to improved perception of voiced speech segments in hearing aid users.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".