Factors Contributing to Instrumental Blends in Orchestral Excerpts
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Bibliographic record
Abstract
Timbral blend is a phenomenon that occurs when two or more concurrent acoustic events produced by distinct sources fuse perceptually and give rise to new timbres. Auditory scene analysis proposes that concurrent grouping cues of onset synchrony, harmonicity, and parallel change in pitch and dynamics are involved in the perceptual fusion of events, but research has also shown that several timbral cues can affect concurrent grouping. We investigated potential factors that may cause different degrees of instrumental blend in orchestral excerpts using rating scales ranging from “unity” to “multiplicity” and from “strongly blended” to “not at all blended.” With linear mixed effects modeling, the factors found to affect ratings included the rating scale used, musical training, timbre class (instrument families involved), the degree of parallelism and onset synchrony of melodic lines involved in the blend, the number of different notes present simultaneously, and several acoustic features related to timbre. Musicians differ from nonmusicians in the use of the multiplicity scale, rating excerpts as more multiple, even if they are fairly well blended, whereas nonmusicians ratings are similar for both scales and to musicians’ ratings of blend. Excerpts with bowed strings and/or woodwinds blend the strongest, followed by combinations involving brass instruments, with excerpts involving percussion and plucked strings blending the least. The important finding of this study on real musical excerpts is in demonstrating the relative roles of the score-based and acoustic factors that are associated with the perception of multiplicity and blend in complex orchestral sonorities as well as the influence of musical training.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it