Analyzing Compositional Style in the Music of The Legend of Zelda: Ocarina of Time
Bibliographic record
Abstract
The soundtrack of The Legend of Zelda: “Ocarina of Time” is rife with fascinating and innovative musical techniques which facilitate player engagement and interactivity. The composer Koji Kondo’s unique style, which often includes the use of modes, chromatic harmony, parallel motion, and proximal voice leading techniques, differentiates this game from others of the same era and efficiently communicates important narrative information like emotion, setting, and trope. Using these musical topics makes Kondo’s works distinct and recognizable. These musical elements also contribute to the game’s overall goal of immersion. By analyzing the characteristics of Kondo’s work, similarly effective stylistic topics can be implemented in new, original compositions to accomplish the same feats as Kondo’s famed soundtracks. The purpose of the project is to develop my personal compositional style which is audibly identifiable while also functioning as nuanced video game music. Some of these nuances of game music include the added challenges of player engagement and the necessity of continuous music; the duration of the music is not predetermined like a live performance or film score. Achieving this goal involves the analysis and recreation of Kondo’s original works, including simulating the limitations of the 1990s era game console hardware. Also, to modernize the findings of this project, similar analysis and experimentation is applied to works of other game composers, exploring the growth and changes in compositional techniques in games throughout the past several decades. The culmination of this research is my own body of original compositions which aim to achieve the goals outlined above. These works include both acoustic and digital pieces of music, and a live interactive suite for medium ensemble.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".