Loudspeakers and headphones: The effects of playback systems on listening test subjects
Bibliographic record
Abstract
Many modern listening test designers disagree on the best playback system to be used when conducting tests. The preference often tends toward headphone-based monitoring in order to minimize the possibility of undesirable acoustical interactions with less than ideal testing environments. On the other hand, most recording and mixing engineers prefer to monitor on loudspeakers, citing a greater ability to make critical decisions on level balances and effects. While anecdotal evidence suggests that differences exist between systems, there is little quantified, perceptually based data to guide both listening test designers and engineers in what differences to expect when alternating between monitoring systems. Controlled tests are conducted with highly trained subjects manipulating the level of solo musical elements against a backing track, using both headphones and loudspeakers. This task serves to make the results equally applicable to critical mixing tasks and rigorous listening tests. The results from both playback systems are compared, showing a defined difference in the mean levels set on the two different monitoring systems. Likewise, the variance seen across subjects is larger when monitoring on headphones than on loudspeakers, lending credence to the hypothesis that tests conducted on one playback system may not be equally applicable to the other.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".