UPPER LIMITS OF AUDITORY MOTION PERCEPTION WITH PERCUSSION SOUNDS
Bibliographic record
Abstract
With the emergence of electroacoustic music in the 1950s, composers started composing music with sounds moving around the listeners. Perceptual studies on sound localization have traditionally focused on static sound sources, but auditory motion perception has garnered increased research attention recently. Research in our lab used synthetic sounds and noises to estimate the upper limit for circular auditory motion perception; that is the velocity above which listeners are no longer able to track sounds that are revolving around them. The current study extends this line of research to more instrumental sounds that are spectrally and temporally more complex than our previous stimuli. Sounds were extracted from a recording of Persephassa (Xenakis, 1969) a percussion piece for six players. Seated at the center of a 16-speaker circular array, 21 participants with normal hearing were asked to indicate in which direction the sound stimuli revolved around them. We used a two-alternative forced choice 2-up, 1-down adaptive procedure to estimate the upper limit for different instrumental sounds and well as pink noise. The upper limits varied as a function of the type of instrument, the family of instrument, and the playing technique. Specifically, the upper limits for three simantras (two wood and one metal) and a piccolo snare drum were significantly lower than for pink noise. The upper limits for two metal instruments (simantra and cymbal) were significantly different from each other. To trace the variation in upper limits to acoustic properties, audio descriptors were extracted from the instrumental sounds using the MIR toolbox. Based on this analysis, we generated new stimuli with varying signal-to-noise ratios and event densities. A follow-up experiment investigated the effect of these features on the upper limit.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".