Perceptual evaluation of multichannel synthesis of moving sounds as a function of rendering strategies and velocity
Bibliographic record
Abstract
Sound-field synthesis for static sound sources has been extensively studied. Recently, dynamic sound sources synthesis has garnered increased attention. Classical sound-field rendering strategies discretize dynamic sound-fields as a sequence of stationary snapshots. The use of discrete multichannel arrays can generate further artifacts with moving sounds. Depending on the technique used, this results in an amplitude modulation due to successive loudspeaker contributions (VBAP) or in multiple comb-filtering (WFS) which could affect localization cues, especially at off-centered listening positions. We first present a detailed description of these artifacts. We then introduce a hybrid rendering strategy combining propagation simulation and VBAP at audio rate. We used this rendering strategy and WFS to synthesize white noise revolving around listeners on a circular 48-loudspeaker array. On each trial, participants had to identify the trajectory (circle, triangle, or square) for velocities ranging from 0.5 to 2 revolutions per second. Performance was well above chance level in all conditions. While WFS outperformed the hybrid rendering strategy at low velocities, no significant differences were observed at high velocities for which participants relied on temporal cues rather than spatial cues. The results highlight how artifacts of the rendering strategies interfere with dynamic sound localization at different velocities.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".