Expressive Performance Rendering for Reduced Music Repetition in Video Games

The study of video game music (or Ludomusicology) is a relatively young field, and audio often receives fewer resources than other disciplines within video game development. As games have increased in length and complexity, this imbalance, combined with the slow adoption of algorithmic or procedural music solutions, has led to a continued reliance on the typical practice of fixed, looping musical content. However, excessive repetition is a documented issue, contributing to listener fatigue, reduced enjoyment, and diminished immersion. This thesis argues that contemporary video games have reached a scale for which traditional implementation methods are increasingly insufficient, making computational approaches to supplementing music necessary.

To investigate this, four studies were conducted. The first, a comparative case study of Final Fantasy VII (SquareSoft, 1997) and its 2020 Remake (Square Enix, 2020), establishes that repetition-related issues persist despite technical advancement. The second study uses reflexive thematic analysis of interviews with professional video game composers to examine barriers to AI adoption. While identifying potential benefits, composers cited concerns regarding creative control and a lack of expressive nuance. The third study addresses these concerns by introducing two novel expressive rendering systems: Cue-Free Express and Cue-Free Express + Pedal. These models enhance symbolic music playback using only pitch and quantised timing data as input to generate expressive variation, significantly improving perceived expressivity over existing baselines. The final study evaluates these systems \textit{in situ} within a role-playing game; results indicate that while expressive variation is less perceptible during gameplay than in isolated listening conditions, it significantly influences player perceptions of repetition, performance, and compositional quality. Overall, this thesis adopts a pragmatic approach to contribute: 1) a framework for analysing repetition; 2) a set of design guidelines for AI music tools; 3) a novel expressive rendering architecture; and 4) the first empirical investigation of expressive rendering in a game context.