Data quality matters: Iterative corrections on a corpus of Mendelssohn string quartets and implications for MIR analysis
Bibliographic record
Abstract
In this paper, we describe a workflow of successive corrections on Optical Music Recognition (OMR) generated MusicXML files and their respective outputs under Music Information Retrieval tasks. The original OMR-generated files of six Mendelssohn String Quartets were initially corrected by individual members of this interdisciplinary group, then reviewed by others to further standardize the quality and music analysis priorities of the team. Four MIR tasks are applied to each round of corrections on this collection: cadence detection, chord labeling, key finding, and monophonic pattern discovery.We measure changes in the outputs of these four MIR tasks from one round of correction to the next in order to evaluate the impact of corrections. Results show that expert revision is more beneficial to some MIR tasks than to others. The resulting corpus of curated MusicXML files is available as an open-source repository under a Creative Commons Attribution 4.0 International License for further MIR research.
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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 | 0.039 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".