Acoustical correlates of perceptual blend in timbre dyads and triads
Bibliographic record
Abstract
Achieving a blended timbre for particular combinations of instruments, pitches, and articulations is a common aim of orchestration. This involves a set of factors that this study jointly assesses by correlating the perceptual degree of blend with the underlying acoustical characteristics. Perceptual blend ratings from two experiments were considered, with the stimuli consisting of: 1) dyads of wind instruments at unison and minor-third intervals and at two pitch levels, and 2) triads of wind and string instruments, including bowed and plucked string excitation. The correlational analysis relied on partial least-squares regression, as this technique is not restricted by the number and collinearity of regressors. The regressors encompassed acoustical descriptors of timbre (spectral, temporal, and spectrotemporal), as well as acoustical descriptors accounting for pitch and articulation. From regressor loadings in principal-components space, the major regressors leading to substantial and orthogonal contributions were identified. The regression models explained around 90% of the variance in the datasets, which was achievable with less than a third of all regressors considered initially. Blend seemed to be influenced by differences across intervals, pitch, and articulation. Unison intervals yielded more blend than did non-unison intervals, and the presence of plucked strings resulted in clearly lower blend ratings than for sustained instrument combinations. Furthermore, prominent spectral features of instrument combinations influenced perceived blend.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".