Predicting blend between orchestral timbres using generalized spectral-envelope descriptions
Bibliographic record
Abstract
Composers rely on implicit knowledge of instrument timbres to achieve certain effects in orchestration. In the context of perceptual blending between orchestral timbres, holistic acoustical descriptions of instrument-specific traits can assist in the selection of suitable instrument combinations. The chosen mode of description utilizes spectral-envelope estimates that are acquired as pitch-invariant descriptions of instruments at different dynamic markings. Prominent local spectral-envelope traits, such as spectral maxima or formants, have been shown to influence timbre blending, involving frequency relationships between local spectral features, their prominence as formants, and constraints imposed by the human auditory system. We present computational approaches to predict timbre blend that are based on these factors and explain around 85% of the variance in behavioral timbre-blend data. Multiple linear regression is employed in modeling a range of behavioral data acquired in different experimental investigations. These include parametric investigations of formant frequency and magnitude relationships as well as arbitrary combinations of recorded instrument audio samples in dyads or triads. The cataloguing of generalized acoustical descriptions of instruments and associated timbre-blend predictions for various instrument combinations could serve as a valuable aid to orchestration practice in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".