The Effects of Varying Directional Bandwidth in Hearing Aid Users' Preference and Speech-in-Noise Performance
Bibliographic record
Abstract
PURPOSE: Directional microphone systems are typically used to improve hearing aid users' understanding of speech in noise. However, directional microphones also increase internal hearing aid noise. The purpose of this study was to investigate how varying directional microphone bandwidth affected listening preference and speech-in-noise performance. METHOD: Ten participants with normal hearing and 10 participants with hearing impairment compared internal noise levels between hearing aid memories with 4 different microphone modes: omnidirectional, full directional, high-frequency directionality with directional processing above 900 Hz, and high-frequency directionality with directional processing above 2000 Hz. Speech-in-noise performance was measured with each memory for the participants with hearing impairment. RESULTS: Participants with normal hearing preferred memories with less directional bandwidth. Participants with hearing impairment also tended to prefer the memories with less directional bandwidth. However, the majority of participants with hearing impairment did not indicate a preference between omnidirectional and directional above 2000 Hz memories. Average hearing-in-noise performance improved with increasing directional bandwidth. CONCLUSIONS: Most participants preferred memories with less directional bandwidth in quiet. Participants with hearing impairment indicated no difference in preference between directional above 2000 Hz and the omnidirectional memories. Speech recognition in noise performance improved with increasing directional bandwidth.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".