A comparison of listener loudspeaker preference ratings based on <i>in</i> <i>situ</i> versus auralized presentations of the loudspeakers
Bibliographic record
Abstract
Auralization methods have several practical and methodological advantages for studying the perception of loudspeaker reproduction in rooms. For subjective measurements of loudspeakers, the auralization should be capable of eliciting the same sound quality ratings as those measured using an original acoustic presentation of a loudspeaker. An experiment was designed to test whether this is possible. Listeners gave preference ratings for both acoustic (in situ) and auralized double-blind presentations of four different loudspeakers, and the results were compared. The auralized presentations were generated from binaural room-scanned impulse responses of the loudspeakers convolved with the music test signals and presented over headphones equipped with a head-tracking device. For both in situ and auralized methods nine trained listeners gave preference ratings for four different loudspeakers using four different programs with one repeat (eight trials in total). The results show that the auralized and in situ presentations generally produced similar loudspeaker preference ratings.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".