Wind noise in hearing aids with directional and omnidirectional microphones: Polar characteristics of behind-the-ear hearing aids
Bibliographic record
Abstract
The purpose of this study was to examine the characteristics of wind noise at the output of in-the-ear, in-the-canal, and completely-in-the-canal hearing aids. The hearing aids were programed to have linear amplification with matching flat frequency responses for directional (DIR) and omnidirectional (OMNI) microphones. The microphone output was then recorded in a quiet wind tunnel when the Knowles electronic manikin for acoustic research (KEMAR) head was turned from 0 degrees to 360 degrees . The overall, 125, 500, and 2000 Hz one-third octave band flow noise levels were calculated and plotted in polar patterns. Correlation coefficients, average differences, and level differences between DIR and OMNI were also calculated. Flow noise levels were the highest when KEMAR was facing the direction of the flow and angles between 190 degrees and 250 degrees . The noise levels were the lowest when the hearing aids were facing the direction of the flow. The polar patterns of DIR and OMNI had similar shapes and DIR generally had higher levels than OMNI. DIR, however, could have lower levels than OMNI in some angles because of its capability to reduce noise in the far field. Comparisons of polar characteristics with behind-the-ear hearing aids, and clinical and engineering design applications of current results are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".