Next generation customizability of music in video games: the creation of an "Artificially Intelligent" audio engine
Bibliographic record
Abstract
Customizability is becoming an increasingly important element of marketing. However, some of the customization options in games can have dramatic consequences. The Xbox 360 introduced the ability to substitute a CD or personal music playlist into any video game produced for the console. Elsewhere, Wharton (2010) has examined the influence that a player's choice of music has on gameplay tactics and on perceived levels of immersion through user-studies. Players specifically chose music for the purpose of relieving anxiety, improving tactics and to experience immersion. Results showed that players were unable to predict what music would improve their experience, however, indicating a lack of understanding about the affective properties of music. Nevertheless, music can strongly influence a player's emotional engagement with a game, and the 'wrong' music can lead to a completely altered affective experience. My aim now is to devise a game audio engine that would read a player's playlist and intelligently insert music into appropriate sections in a game, so that combat scenes would use fast-pace dramatic and dark music, while exploration scenes may be more ambient and calm. The presentation will outline the methods for quantifying musical mood, audio extraction techniques, as well as plans to integrate the information into a game engine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".