Bibliographic record
Abstract
Since their inception, music and sound in digital games have predominantly played supportive roles, with game states and events typically triggering the playback of sounds or changes in the music. This survey shifts the perspective to games where this relationship is reversed: music and sound are at the forefront, driving interactions and shaping the flow of gameplay. These sound-first games are significantly less common than their traditional counterparts and span a narrower range of gameplay styles and genres. Most often, they fall under the category of music and rhythm games that focus on performing timed actions synchronized with music. Beyond this genre, only a small number of platformers, shooters, and RPGs have adopted a sound-led paradigm, while a few music-making and educational applications feature playful approaches, placing them in a space that blurs the boundaries between games and music production tools. In this survey we address the lack of current categorizations for sound-first games by identifying examples and classifying them by genre, form of audio interaction, and style of control. It also identifies areas for future growth, including the development of richer sound-based mechanics, the fuller integration of spatial audio as a core gameplay element, and the exploration of more nuanced listening modes that extend beyond simple sound triggers.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".