On the Use of Wide Dynamic Range Compression and Other Algorithms to Improve Hearing Protection of Workers with Hearing Impairment: A Preliminary Study on Speech Intelligibility
Bibliographic record
Abstract
Despite being affected by one of the most common occupational diseases worldwide, workers with hearing loss remain without any consensual solutions when it comes to protecting their residual hearing while working safely and efficiently. One solution to be explored is to adapt signal processing algorithms commonly used in hearing aids for hearing protection applications and to implement them on a research platform for evaluation. The algorithms to be assessed include Wide Dynamic Range Compression (WDRC) to amplify sound based on the user’s hearing loss while protecting their hearing, Modulation-Based Digital Noise Reduction (MBDNR) to enhance speech, and an Automatic Gain Control (AGC) to protect from excessive exposure. A major need for workers is the ability to communicate in noise. Different parameters and algorithms combination have been proven to affect speech intelligibility in hearing aid devices. To select the proper combination and parameters before hardware implementation, this study explores the impact of different algorithm configurations on speech intelligibility in factory noise in a laboratory environment. Participants either without hearing impairment or with mild to moderate hearing loss undergo five different Hearing in Noise Test (HINT) in order to measure their Speech Reception Thresholds (SRTs) using the MATLAB Speech Test Environment (MSTE). Differences in the SRTs obtained when simulating different hearing devices and other metrics are analyzed to highlight a potential benefit of the signal processing algorithms in terms of speech intelligibility improvement in a typical factory noise. © 2023, Canadian Acoustical Association. All rights reserved.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".