Evaluating the principal spectral components positioning a virtual sound source on a cone of confusion
Bibliographic record
Abstract
Although many studies have attempted to identify spectral cues to sound-source direction for the entire sphere of possible incidence angles, few studies have focused their attention on directional hearing for virtual sources positioned on cones of confusion. These regions of space, defined by relatively constant interaural time and intensity differences, provide an ideal subset of incidence angles for testing the importance of the spectral cues to direction allowing for up/down and front/rear distinctions between sources at constant angular distance from the median plane. In this study, head-related transfer functions (HRTFs) measured on a well-lateralized cone of confusion were decomposed into principal spectral components putatively responsible for positioning a virtual sound source, and their related angle-dependent scores that describe the relative contribution of each shape to the total spectral variation. The findings can be summarized as follows: For the set of ipsilateral HRTFs the most significant set of scores shows sinusoidal variation with angle, and could potentially encode a front-back cue, though with the extrema rotated by about 30 deg. The most significant score from an analysis of interaural spectral differences seems to capture a head-shadowing effect which is most extreme in the lower rear.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".