Editorial: JCMS Special Issue of the first Conference on AI Music Creativity
Bibliographic record
Abstract
The International conference on AI Music Creativity (AIMC, https://aimusiccreativity.org/) is the merger of the international workshop on Musical Metacreation MUME (https://musicalmetacreation.org/) and the conference series on Computer Simulation of Music Creativity (CSMC, https://csmc2018.wordpress.com/). This special issue gathers selected papers from the first edition of the conference along with paper versions of two of its keynotes.This special issue contains six papers that apply novel approaches to the generation and classification of music. Covering several generative musical tasks such as composition, rhythm generation, orchestration, as well as some machine listening task of tempo and genre recognition, these selected papers present state of the art techniques in Music AI. The issue opens up with an ode on computer Musicking, by keynote speaker Alice Eldridge, and Johan Sundberg's use of analysis-by-synthesis for musical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".