Editorial for TISMIR Special Collection: AI and Musical Creativity
Bibliographic record
Abstract
This special issue focuses on research developments and critical thought in the domain of artificial intelligence (AI) applied to modeling and creating music. It is motivated by the AI Song Contests of 2020 and 2021, in which the four guest editors adjudicated or participated among many teams from around the world. The 2020 edition had 13 submissions and the 2021 edition had 38. The 2022 edition is now being planned. These unique events provide exciting opportunities for AI music researchers to test the state of the art and push the boundaries of what is possible, within the context of music creation. They portend a future when humans and machines work together as partners in music creation. Maybe “portend” is not the right term, but we must not think that the future of AI and music is only warm and fuzzy. It is important and timely to consider how we, in local and global contexts, can effectively and ethically develop and apply AI in contexts of music creation.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.040 | 0.024 |
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".