A Taxonomy of Orchestral Grouping Effects Derived from Principles of Auditory Perception
Bibliographic record
Abstract
The study of timbre and orchestration in symphonic music research is underexplored, and few theories attempt to explain strategies for combining and contrasting instruments and the resulting perception of orchestral structures and textures. An analysis of orchestration treatises and musical scores reveals an implicit understanding of auditory grouping principles by which many orchestration techniques give rise to predictable perceptual effects. We present a novel theory formalized in a taxonomy of devices related to auditory grouping principles that appear frequently in Western orchestration practices from a range of historical epochs. We develop three classes of orchestration analysis categories: concurrent grouping cues result in blended combinations of instruments; sequential grouping cues result in melodic lines, the integration of surface textures, and the segregation of melodies or stratified (foreground and background) layers based on acoustic (dis)similarities; segmental grouping cues contrast sequentially presented blocks of materials and contribute to the creation of perceptual boundaries. The theory predicts orchestration-based perceptual structuring in music and may be applied to music of any style, culture, or genre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".