Understanding Feature Importance in Musical Works: Unpacking Predictive Contributions to Cluster Analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cluster analysis provides insight into musical patterns in composition, performance, and perception. Despite its wide adoption in music research, understanding how specific features affect clustering solutions remains challenging. For example, features such as mode (i.e., major/minor), timing, signal amplitude, and pitch are often intercorrelated, making it difficult to understand their specific role within different clusters. To demonstrate how accumulated local effects (ALEs) can help with this challenge, here we analyze 48 excerpts from complete sets of preludes by Bach and Chopin, showing how specific features contribute to two- and three-cluster analyses. These exploratory analyses reveal that ALEs can identify salient or subtle data patterns from cluster analyses by tracking how changes in features affect cluster membership. We explore these insights in visualizations quantifying feature importance and an interactive companion application ( https://maplelab.net/feature-importance/ ) featuring the analyzed audio. Following a demonstration of this method, we suggest how it can be applied to explore topics of interest to researchers in music information retrieval, empirical musicology, and music cognition alike.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it