Using quadratic programming to estimate feature relevance in structural analyses of music
Bibliographic record
Abstract
To identify repeated patterns and contrasting sections in music, it is common to use self-similarity matrices (SSMs) to visualize and estimate structure. We introduce a novel application for SSMs derived from audio recordings: using them to learn about the potential reasoning behind a listener's annotation. We use SSMs generated by musically-motivated audio features at various timescales to represent contributions to a structural annotation. Since a listener's attention can shift among musical features (e.g., rhythm, timbre, and harmony) throughout a piece, we further break down the SSMs into section-wise components and use quadratic programming (QP) to minimize the distance between a linear sum of these components and the annotated description. We posit that the optimal section-wise weights on the feature components may indicate the features to which a listener attended when annotating a piece, and thus may help us to understand why two listeners disagreed about a piece's structure. We discuss some examples that substantiate the claim that feature relevance varies throughout a piece, using our method to investigate differences between listeners' interpretations, and lastly propose some variations on our method.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".