Correcting Large-Scale OMR Data with Crowdsourcing
Bibliographic record
Abstract
This paper discusses several technical challenges in using crowdsourcing for distributed correction interfaces. The specific scenario under investigation involves the implementation of a crowd-sourced adaptive optical music recognition system (Single Interface for Music Score Searching and Analysis project). We envisage the distribution of correction tasks beyond a single workstation to potentially thousands of users around the globe. This will have the effect of producing human-checked transcriptions, as well as significant quantities of human-provided ground-truth data, which may be re-integrated into an adaptive recognition process, allowing an OMR system to "learn" from its mistakes. Drawing from existing crowdsourcing approaches and user interfaces in music (e.g., Bodleian Libraries) and non-music (e.g., CAPTCHAs) applications, this project aims to develop a scientific understanding of what makes crowdsourcing work, how to entice, engage and reward contributors, and how to evaluate their reliability. While results will be considered based on the specific needs of SIMSSA, such knowledge can be useful to a variety of musicological investigations that involve labour-intensive methods.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".