Interaction between critical listening environment acoustics and listener reverberation preference
Bibliographic record
Abstract
Reverberation is a central effect in many modern music productions. In the case of classical music, it may even be the only effect used. There is, however, minimal literature concerning the interaction between reverberation preference and the listening environment used during critical mixing tasks. In order to explore this critical interaction, a group of highly trained subjects are tasked with adding reverberation to dry, premixed stereo program material in two different acoustic environments: a recording studio control room and a highly reflective room. The control room is representative of most studios, with an RT of approximately 200 ms. The reflective environment more closely approximates an untreated residential room, with an RT of over 350 ms, with a marked increase in lateral energy. Somewhat predictably, the mean preferred reverberation level is higher in a less reverberant environment, but the distributions of reverberation level preference are shown to be narrower for the more reflective mixing environment. The time it takes for subjects to reach a decision is similar in both environments, but the reflective environment shows a longer period of adaptation at the beginning of each trial set.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".