Focality of sound source placement by higher (9 <sup>th</sup> ) order ambisonics and perceptual effects of spectral reproduction errors
Bibliographic record
Abstract
Higher-order ambisonic rendering is an increasingly common soundscape reproduction technique that, in theory, enables presentation of virtual sounds at nearly any location in 3D space, relatively unconstrained by the veridical locations of loudspeakers. We evaluated whether 3D sound reproduction through a 9 th -order ambisonic loudspeaker array was indeed sufficiently accurate to probe the limits of human spatial perception. We first estimated minimum audible angles for human listeners for a variety of reference points on the horizontal plane. We demonstrated that the system can reproduce sounds with a spatial resolution that is equal or superior to the limits of human acuity, at least on the horizontal plane at the front. Importantly, the resolution of ambisonic reproduction appeared equivalent for regions of the system with high and low loudspeaker density. We also estimated localization cues at the same locations and showed that although localization cues for low-frequency components were well preserved, they were somewhat distorted for components above 4000 Hz. Finally, we provide evidence that these high-frequency distortions can serve elevation cues by human listeners. In summary, we showed that a 9 th order ambisonic system is able to render highly focal sound sources, and that high-frequency reproduction distortions may introduce unwanted localization cues.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".