Objective Assessment of Companding Architecture for Assistive Hearing Devices
Bibliographic record
Abstract
Individuals with auditory neuropathy spectrum disorder (ANSD) or auditory processing disorders (APDs) often suffer from temporal and spectral processing deficits leading to degraded speech perception, especially in the presence of background noise. Evidence exists that the exaggeration of temporal and spectral cues may enhance intelligibility, although a comprehensive evaluation of envelope and spectral enhancement algorithms is currently lacking. In the present study, the effect of a companding architecture on speech perception, with and without an additional noise reduction algorithm, was investigated with sentence-level-stimuli in different background noise conditions. The companding structure was assessed objectively using the speech-to-reverberation modulation energy ratio (SRMR), which is a non-intrusive metric for speech quality and intelligibility based on a modulation spectral representation of the speech signal. Results of the present study demonstrated that the companding structure improved the predicted speech intelligibility score for all background noise conditions. Furthermore, results revealed that the application of the Minimum-Mean-Square (MMSE) noise reduction algorithm (Ephraim & Malah, IEEE Trans. Acoust, 1984, pp. 1109–1121), which was previously shown to produce lesser musical noise, can significantly improve the performance of companding structure for all Signal-to-Noise ratio (SNR) conditions. These results can potentially guide the choice and activation of companding structure in assistive hearing devices.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".