Bibliographic record
Abstract
Pattern discovery is an important part of computational music-processing systems. The discovery of patterns repeated within a single piece is an important step to segmentation according to thematic structures (Ruwet 1966). Patterns found within a few works may be signatures that can be instantiated for style emulation of novel musical material (Cope 1991; Rowe 1993) and can reveal a deep similarity in musical material. Patterns that are conserved across many pieces in a large corpus can represent structural building blocks and used for comparative style analysis and music genre recognition (Huron 2001; Conklin and Anagnostopoulou 2001; Lin etal. 2004). Pattern discovery methods can be discussed according to the expressiveness of patterns in particular, the levels of abstraction permitted by pattern components. Many approaches are restricted to a representation in which every pattern component is described using the same musical attribute: pitch, duration, interval, or fixed combinations of these (e.g., linked interval/duration, etc.). In these approaches, an event has only one possible representation, and therefore patterns can be efficiently found using general string algorithms (Gusfield 1997) after transforming the corpus to strings of attribute values. Recent methods have considered whether this restriction can be relaxed by allowing patterns with heterogeneous components and subsumption relations among possible pattern components (Lartillot 2004; Cambouropoulos et al. 2005; Conklin and Bergeron 2007). The need for such patterns can be motivated with a few melodic fragments (see Figure 1 ) from the music of the famous twentieth-century French singer and songwriter Georges Brassens (1921-1981). In both pairs of fragments, the description of events by melodic interval or melodic contour alone is inadequate. Though the fragments within each pair have a common duration pattern, there is no melodic interval pattern that spans the complete fragments, though some events do have conserved melodic intervals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".