Bibliographic record
Abstract
In computer games, music often serves to create a more immersive and captivating experience for the target audience. As such, it often needs to adapt in real-time to changes in the game state. Otherwise, it might not blend well with the game environment and might even be detrimental to the players' experience of the game. In this paper, we describe IMTool: an open framework for interactive music composition. It includes an authoring tool whose interface is designed to maximize composers' productivity and a music engine which can be integrated to a game engine through an easy-to-grasp Application Programming Interface (API). Our model is based on finite state machines. We introduce a hybridization between extended and probabilistic finite state machines. This results in automata which include both registers and probabilities. The former allow to create nonlinear music which can adapt to the context of the game. The latter allow to create variations in musical themes more easily. The main motivation of our work is to create a reusable system that may facilitate the implementation of interactive music in future computer games.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.060 |
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".