Ancient Chinese zither (guqin) music recovery with support vector machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Chinese zither, called guqin, has existed for over 3,000 years and always played an important role in Chinese social history. An interesting but unfortunate fact is that the traditional notation of guqin music does not provide the duration information for each music note which requires the player to learn from his teacher and memorize. As a result, among several thousands of compositions that have been created and recorded with guqin music notation, only around 100 of them are still being played today. In this article we use a machine learning method to study the guqin music recovery problem which tries to use the guqin music notation to recover the duration of each music note. Information provided by the music note is used as features to predict the duration information with a support vector machine. The experimental result shows that our system can predict with fair accuracy, and can be used as a valuable reference for human guqin masters to recover guqin music.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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