Bibliographic record
Abstract
This dissertation features methods of analyzing symbolic music, focused on n-gram-based approaches, as this representation resembles the most text and natural languages. The analysis of similarities between several text and music corpora is accompanied with implementation of text-based methods for problems of composer classification and symbolic music similarity definition. Both problems contain thorough evaluation of performance of the systems with comparisons to other approaches on existing testbeds. It is also described how one can use this symbolic representation in conjunction with genetic algorithms to tackle problems like melody generation. The proposed method is fully automated, and the process utilizes n-gram statistics from a sample corpus to achieve it. A method of visualization of complex symbolic music pieces is also presented. It consist of creating a self similarity matrix of a piece in question, revealing dependencies between voices, themes and sections, as well as music structure. A fully automatic technique of inferring music structure from these similarity matrices is also presented The proposed structure analysis system is compared against similar approaches that operate on audio data. The evaluation shows that the presented structure analysis system outperformed significantly all audio-based algorithms available for comparison in both precision and recall.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".