PatternFinder
Bibliographic record
Abstract
Content-Based Music Retrieval (CBMR) for symbolic music aims to find all similar occurrences of a musical pattern within a larger database of symbolic music. To the best of our knowledge there does not currently exist a distributable CBMR software package integrated with a music analysis toolkit that facilitates extendability with new CBMR methods. This project presents a new MIR tool called "PatternFinder" satisfying these goals. PatternFinder is built with the computational musicology Python package music21, which provides a flexible platform capable of working with many music notation formats. To achieve polyphonic CBMR, we implement seven geometric algorithms developed at the University of Helsinki---four of which are being implemented and released publicly for the first time. The application of our MIR tool is then demonstrated through a musicological investigation of Renaissance imitation masses, which borrow melodic or contrapuntal material from a pre-existing musical work. In addition, we show Pattern-Finder's ability to find a contrapuntal pattern over a large dataset, Palestrina's 104 masses. Our investigations demonstrate the relevance of our tool for musicological research as well as its potential application for locating music within digital music libraries.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".