Instrument Timbre Combinations Influence the Relative Prominence of Perceptual Layers in Orchestral Music
Bibliographic record
Abstract
The stratification of layers of differing prominence (foreground/background) is a common technique in orchestration. Musicians heard 23 excerpts containing foreground and background layers as previously determined by music analysts. A given layer comprised either a single auditory stream of one or more blended instruments or a harmonic or rhythmic background. Two-layer excerpts had either the same, overlapping, or different instrument families (timbre class). First, musicians rated the perceived degree of segregation of musical materials in two-layer and single-stream excerpts. Second, they heard each of the two layers in isolation and then together and rated the relative prominence of the layers. Heterogeneous instrument combinations yielded the greatest difference in relative prominence, followed by overlapping and then homogeneous combinations. Acoustic and score-based descriptors were extracted to quantify their relative contribution to perceptual stratification. Timbre class and between-layer differences in timbre and dynamics played a role, providing evidence of how timbral differences enhance relative prominence in orchestral music. Perceptual segregation was positively but very weakly related to relative prominence, supporting findings that although segregation is necessary to form layers, this mechanism is separable from that which places the streams into the same representational space to allow for the assessment of their relative prominence.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".