A Cross-Validated Study of Modelling Strategies for Automatic Chord Recognition in Audio.
Why this work is in the frame
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Bibliographic record
Abstract
Although automatic chord recognition has generated a number of recent papers in MIR, nobody to date has done a proper cross validation of their recognition results. Cross validation is the most common way to establish baseline standards and make comparisons, e.g., for MIREX competitions, but a lack of labelled aligned training data has rendered it impractical. In this paper, we present a comparison of several modelling strategies for chord recognition, hiddenMarkov models (HMMs) and conditional random fields (CRFs), on a new set of aligned ground truth for the Beatles data set of Sheh and Ellis (2003). Consistent with previous work, our models use pitch class profile (PCP) vectors for audio modelling. Our results show improvement over previous literature, provide precise estimates of the performance of both old and new approaches to the problem, and suggest several avenues for future work.
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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 | 0.001 | 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 | 0.007 |
| Open science | 0.000 | 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 it