DECOUPLED LOUDNESS AND RANGE CONTROL FOR A SOURCE LOCATED WITHIN A SMALL VIRTUAL ACOUSTIC ENVIRONMENT
Bibliographic record
Abstract
For headphone-based spatial auditory display systems, binaural synthesis of sound localization cues typically use source reproduction level as the primary control for source range. This approach can be quite effective when indirect sound is simulated in order to externalize virtual sources within a small virtual acoustic environment. A computationally efficient simulation solution is described here that does not rely solely upon the sound reproduction level of the source to control source range (i.e., perceived egocentric distance), and provides an extremely economical synthesis of the indirect sound component that is effective in creating externalized spatial auditory images. The performance of the solution has been psychophysically validated using indirect scaling methods that required experimental listeners to compare two displayed sound stimuli and report which of the two was the louder or the closer. In particular, it was shown that the simulation allows for decoupled loudness and range control for a source located near the listener’s head, so that equally loud sources can be positioned at varying source range. Likewise, within certain limits, source loudness may be varied while holding source range constant. This performance feature has benefits for auditory display applications for which selective attention should be supported for a spatially distributed set of virtual sound sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".