Perceptual evaluation of a virtual acoustic room model
Bibliographic record
Abstract
Two experiments were conducted to perceptually evaluate a virtual acoustic room model implemented using a virtual microphone technique for the direct path and reflections, the image-source model for the reflection geometry and a Feedback Delay Network reverberator. Experiment 1 aimed to determine the best equalization profile for different room sizes. Anechoic recordings of 7 solo instruments were processed to generate 5 room sizes with profiles corresponding to 5 different absorption settings. Fourteen sound engineers and musicians were asked to rate the resulting stimuli in terms of how realistic each setting was for the instrument in that space. The highest ranked absorption setting for each room size was selected for Experiment 2 to investigate whether listeners could identify different rooms sizes across different instruments. Using a free sorting task, 15 musicians were presented with 7 different solo instruments in 5 different room sizes and asked to group them by type of space in which the instruments were performed. The analysis of the dissimilarity matrices indicates that participants were able to organize sounds by room sizes, but the two largest rooms tended to be clustered together. The results show that the virtual acoustic model is capable of rendering realistic room effects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".