Marking musical dictations using the edit distance algorithm
Bibliographic record
Abstract
Abstract Musical dictations for ear training and training in music writing form a key practice of basic musical training. Marking students' dictation exercises for large groups of students can require a lot of work. In this paper, we present a tool, called CADiM, that can help automate the marking of such musical dictations. The edit distance, which computes the similarity between any two strings, has been used in various areas such as string/text analysis, protein/genome matching in bio‐computing and musical applications, for example music retrieval or musicological analysis. CADiM's marking algorithm is based on an earlier edit distance proposed for musical sequences, but adapted to reflect the marking heuristic used by a domain expert's specific approach to musical training. Computing an edit distance on musical scores requires using an appropriate representation. More precisely, given our specific context, a symbolic representation is required. We use MusicXML, an XML application for standard Western music notation. Given a Document Type Definition for MusicXML, existing Java tools can generate a MusicXML parser. Such a parser, given appropriate input files, then generates an intermediate form (DOM object) on which analyses and transformations are performed in order to compute the edit distance. In turn, the edit distance is used to give a mark as well as identify the key errors. CADiM has been applied to a number of test cases and the results compared with those obtained by a domain expert. Overall, the results are promising, namely, only 3% difference between the domain expert's marks and those produced by CADiM. Copyright © 2006 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".