Instrument Timbre Enhances Perceptual Segregation in Orchestral Music
Bibliographic record
Abstract
Timbre perception and auditory grouping principles can provide a theoretical basis for aspects of orchestration. In Experiment 1, 36 excerpts contained two streams and 12 contained one stream as determined by music analysts. Streams—the perceptual connecting of successive events—comprised either single instruments or blended combinations of instruments from the same or different families. Musicians and nonmusicians rated the degree of segregation perceived in the excerpts. Heterogeneous instrument combinations between streams yielded greater segregation than did homogeneous ones. Experiment 2 presented the individual streams from each two-stream excerpt. Blend ratings on isolated individual streams from the two-stream excerpts did not predict global segregation between streams. In Experiment 3, Experiment 1 excerpts were reorchestrated with only string instruments to determine the relative contribution of timbre to segregation beyond other musical cues. Decreasing timbral differences reduced segregation ratings. Acoustic and score-based descriptors were extracted from the recordings and scores, respectively, to statistically quantify the factors involved in these effects. Instrument family, part crossing, consonance, spectral factors related to timbre, and onset synchrony all played a role, providing evidence of how timbral differences enhance segregation in orchestral music.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".