Procedural Music Generation Systems in Games

Luo S, Reiss J. Procedural Music Generation Systems in Games. In: 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio. Audio Engineering Society; 2025. Paper 24. Available from: https://aes.org/publications/elibrary-page/?id=23013

@inproceedings{Luo2025_23013,
  author = {Luo, Shangxuan and Reiss, Joshua},
  title = {{Procedural Music Generation Systems in Games}},
  booktitle = {2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio},
  note = {Paper 24},
  year = {2025},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23013}
}

TY  - CPAPER
TI  - Procedural Music Generation Systems in Games
AU  - Luo, Shangxuan
AU  - Reiss, Joshua
T2  - 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio
M1  - Paper 24
PY  - 2025
DA  - 2025/09/02
UR  - https://aes.org/publications/elibrary-page/?id=23013
PB  - Audio Engineering Society
LA  - en
AB  - 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 reduce development overhead, 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 real-time integration. This paper bridges the gap by presenting a systematic overview of current PMG techniques, 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 design, more comprehensive quality evaluation protocols, and improved tool integration to provide actionable insights for developers, composers, and researchers seeking to advance PMG in game contexts.
ER  -