A supervised learning AI model for automated holistic vocal performance feedback
Bibliographic record
Abstract
Scalable and effective music education requires giving fast and accurate feedback on student audio performances. Manual feedback from teachers is often subjective and, therefore, sometimes inaccurate. Current technological feedback mechanisms evaluate whether a student is correct on a single note rather than the entire music piece, lacking cumulative or numerical feedback. This paper presents a machine learning model for automatically grading vocal music recordings cumulatively on pitch and rhythm, given a reference piece of music. The model predicts a numerical grade for the performance of a reference piece of music and employs a correspondence algorithm to provide granular feedback (e.g., pitch mismatch, timing errors, missed notes). When tested on the MAST Melody Dataset, the ML model achieved more than 80% accuracy in scoring performances where human judges had consensus. Additionally, analysis revealed 20.85% outlier samples in human grading, highlighting subjectivity in manual assessment. The proposed system demonstrates the feasibility of objective vocal performance evaluation, while exposing limitations in current grading practices..
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".