Hearing aid gain prescriptions balance restoration of auditory nerve mean-rate and spike-timing representations of speech
Bibliographic record
Abstract
Linear and nonlinear amplification schemes for hearing aids have thus far been developed and evaluated based on perceptual criteria such as speech intelligibility, sound comfort, and loudness equalization. Finding amplification schemes that optimize all of these perceptual metrics has proven difficult. Using a physiological model, Bruce et al. [1] investigated the effects of single-band gain adjustments to linear amplification prescriptions. Optimal gain adjustments for model auditory-nerve fiber responses to speech sentences from the TIMIT database were dependent on whether the error metric included the spike timing information (i.e., a time-resolution of several microseconds) or the mean firing rates (i.e., a time-resolution of several milliseconds). Results showed that positive gain adjustments are required to optimize the mean firing rate responses, whereas negative gain adjustments tend to optimize spike timing information responses. In this paper we examine the results in more depth using a similar optimization scheme applied to a synthetic vowel /E/. It is found that negative gain adjustments (i.e., below the linear gain prescriptions) minimize the spread of synchrony and deviation of the phase response to vowel formants in responses containing spike-timing information. In contrast, positive gain adjustments (i.e., above the linear gain prescriptions) normalize the distribution of mean discharge rates in the auditory nerve responses. Thus, linear amplification prescriptions appear to find a balance between restoring the spike-timing and mean-rate information in auditory-nerve responses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".