The perception and measurement of headphone sound quality
Bibliographic record
Abstract
We recently completed a 7-year research project aimed at understanding the perception and measurement of headphone sound quality. A virtual headphone listening test method was developed to provide controlled, double-blind comparisons of different models of headphones and target response curves using a large number of trained and untrained listeners in USA, Canada, Germany, and China. From these data, we identified a new headphone target curve that is preferred by the majority of listeners. Statistical models were developed that predict listeners’ headphone sound quality ratings based on objective headphone measurements. More recently, cluster analysis of headphone listening test data has shown there are three segments or classes of listeners based on similarities in their headphone sound preferences. Both demographic (i.e., age, gender, listening experience) and acoustic factors are associated with membership in each headphone segment. This information can help guide future headphone design that is aimed at a specific class or segment of listener.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".