Procedural Music Generation Systems in Games

TL;DR

This paper provides a comprehensive overview of Procedural Music Generation in games, analyzing current techniques, challenges, and future directions to enhance integration, quality, and applicability in game development.

Contribution

It offers a systematic taxonomy and comparative analysis of PMG methods, bridging research and practical application in game music generation.

Findings

Identifies key challenges in algorithm implementation and music quality.

Highlights the importance of task-oriented and context-aware design.

Suggests future research directions for improved PMG systems.

Abstract

Procedural Music Generation (PMG) is an emerging field that algorithmically creates music content for video games. By leveraging techniques from simple rule-based approaches to advanced machine learning algorithms, PMG has the potential to significantly improve development efficiency, provide richer musical experiences, and enhance player immersion. However, academic prototypes often diverge from applications due to differences in priorities such as novelty, reliability, and allocated resources. This paper bridges the gap between research and applications by presenting a systematic overview of current PMG techniques in both fields, offering a two-aspect taxonomy. Through a comparative analysis, this study identifies key research challenges in algorithm implementation, music quality and game integration. Finally, the paper outlines future research directions, emphasising task-oriented and context-aware design, more comprehensive quality evaluation methods, and improved research tool integration to provide actionable insights for developers, composers, and researchers seeking to advance PMG in game contexts.