Weighting of interaural time difference and interaural level difference cues in wide-band stimuli with varying low and high frequency energy balance
Bibliographic record
Abstract
Wideband stimuli carry both interaural time and level difference (ITD and ILD) sound location cues. Previously, listener weighting of those cues has only been measured for low-pass, high-pass, and flat spectrum wide-band conditions [Macpherson and Middlebrooks, JASA (2002)]. In this study, we determined how weighting of ITD and ILD cues varied with the low- and high-frequency energy balance in wide-band stimuli. Listeners reported locations of targets that were presented over headphones using individual head related transfer functions. ITD and ILD cues were manipulated by attenuating or delaying the sound at one ear (by up to 300μs or 10dB), and the final weight was computed by comparing the listener's localization response bias to the imposed cue bias. Stimuli were 100-ms bursts of noise whose spectra were flat from 0.5 to 2 kHz and from 4 to 16 kHz with a level difference between those low- and high-frequency ranges varying in 10-dB steps from −30 to + 30 dB. ILD weight increased (from ~0.5 to 1.5 deg/dB) with increasing high-frequency energy, but ITD weight was constant (~0.08 deg/us) across spectral profiles. The results suggest that in wideband stimuli, weighting of ILD is more stimulus dependent than weighting of ITD.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".