Accurate sound localization via head movements in listeners with precipitous high-frequency hearing loss.
Bibliographic record
Abstract
Information about sound source location in the vertical plane is available via the direction-dependent filtering performed by the outer ears, but errors of localization such as front/rear reversals can occur when stimuli contain a limited range of frequencies or when high frequencies are inaudible due to hearing impairment. Information about front/rear sound source location is also available in the relationship between the rotation of the head and the resulting changes in interaural time and level differences. We have shown previously [Macpherson, J. Acoust. Soc. Am. 125, 2691(A) (2009)] that in normally hearing listeners, a minimum head movement angle (MHMA) of 5–10 deg is sufficient for accurate front/rear localization of low-frequency (0.5–1 kHz) noise-band targets. In the present study, we measured MHMAs for low-frequency and wideband (0.5–16 kHz) targets in listeners with near-normal low-frequency thresholds but precipitous hearing loss above 1–2 kHz. Neither stimulus could be localized accurately by these listeners without head movement, but for both stimuli, MHMAs of 5–10 deg sufficed for accurate localization at a rotation velocity of 50 deg/s. MHMAs increased with increasing rotation velocity similarly to those of normally hearing listeners. The results suggest that listeners with normal hearing and with high-frequency loss benefit similarly from dynamic localization cues. [Work supported by the NSF.]
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".