A Methodology for the Computational Evaluation of Style Imitation Algorithms
Bibliographic record
Abstract
We investigate Musical Metacreation algorithms by applying Music Information Retrieval techniques for comparing the output of three off-line, corpus-based style imitation algorithms. The first is Variable Order Markov Chains, a statistical model; second is the Factor Oracle, a pattern matcher; and third, MusiCOG, a novel graphical model based on perceptual processes. Our focus is on discovering which musical biases are introduced by the algorithms, that is, the characteristics of the output which are shaped directly by the formalism of the algorithms and not by the corpus itself. We describe META-MELO, a system that implements the three algorithms, along with a methodology for the quantitative analysis of algorithm output, when trained on a corpus of melodies in symbolic form. Results show that the algorithms’ output are indeed different, although none of them encompass completely the full feature-set belonging to the style of the corpus. We conclude that this methodology is promising for aiding in the informed application and development of generative algorithms for music composition problems.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".