The Sweet Sounds of Syntax: Towards Investigating Hierarchical Structures in Music
Bibliographic record
Abstract
Music comprises connected sequences of hierarchically-related sonic events, roughly analogous in their timescales and construction to the words, phrases and sentences found in language. The concept of “grammatical correctness” in music is somewhat more challenging to define, however—in part due to the nature of structure and meaning in music and how it varies from language. Work such as Lerdahl and Jackendoff’s (1983) seminal Generative Theory of Tonal Music (seek to analyze musical structures with a fine degree of detail reminiscent of linguistic theory; most notably, they describe structures of implied “prolongation” of stable musical events across a musical passage, understood intuitively by listeners as the sense of building expectation and resolution felt when listening to a piece of music. Modelling the organization of musical cognition, Lerdahl and Jackendoff suggest rules of well-formedness and preference to help define these structures; this in turn provides a framework for investigating exactly how the mind and brain process the structure of musical ideas as they unfold before our ears. The current research developed a new methodology and stimulus paradigm, rooted in GTTM, for investigating the online processing of hierarchical structures in tonal music, analogous to those of linguistic syntax—informed by prior research such as Ding et al.’s (2016) neurolinguistics study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".