Pattern Clustering in Monophonic Music by Learning a Non-Linear Embedding From Human Annotations
Bibliographic record
Abstract
Musical pattern discovery algorithms find instances of repetition in symbolic music, allowing for some user-specifiable amount of variation between identified repetitions; however, they can yield an intractably large number of discovered patterns when allowing for even small amounts of variation. This is commonly addressed by defining some heuristic notion of pattern significance, and returning only the most significant patterns. This paper develops a method of pattern discovery that models human judgement of what constitutes a significant pattern by incorporating annotations of repeated patterns, avoiding the need to design heuristics. We take pattern discovery as a clustering task, where the input is a set of passages of monophonic music, represented as vectors of extracted features, and the output clusters correspond to discovered patterns. The human annotations are used to train a neural network to learn a low-dimensional embedding of the feature space that maps passages of music close together when they are occurrences of the same ground-truth pattern. The results of this approach match up with the annotations significantly better than the results of an approach using clustering without subspace learning. We provide examples of the types of patterns that this method tends to discover and discuss its feasibility and practicality as a tool for extracting useful information about repetitive structure in 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".