Sound localization in noise and sensitivity to spectral shape
Bibliographic record
Abstract
Individual differences exist in sound localization performance even for normal-hearing listeners. Some of these differences might be related to acoustical differences in localization cues carried by the head related transfer functions (HRTF). Recent data suggest that individual differences in sound localization performance could also have a perceptual origin. The localization of an auditory target in the up/down and front/back dimensions requires the analysis of the spectral shape of the stimulus. In the present study, we investigated the role of an acoustic factor, the prominence of the spectral shape ("spectral strength") and the role of a perceptual factor, the listener's sensitivity to spectral shape, in individual differences observed in sound localization performance. Spectral strength was computed as the spectral distance between the magnitude spectrum of the HRTFs and a flat spectrum. Sensitivity to spectral shape was evaluated using spectral-modulation thresholds measured with a broadband (0.2-12.8 kHz) or high-frequency (4-16 kHz) carrier and for different spectral modulation frequencies (below 1 cycle/octave, between 1 and 2 cycles/octave, above 2 cycles/octave). Data obtained from 19 young normal-hearing listeners showed that low thresholds for spectral modulation frequency below 1 cycle/octave with a high-frequency carrier were associated with better sound localization performance. No correlation was found between sound localization performance and the spectral strength of the HRTFs. These results suggest that differences in perceptual ability, rather than acoustical differences, contribute to individual differences in sound localization performance in noise.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".