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.

翻译：程序化音乐生成（PMG）是一个新兴领域，旨在通过算法为视频游戏创作音乐内容。通过利用从简单的基于规则方法到先进的机器学习算法的技术，PMG有潜力显著提高开发效率、提供更丰富的音乐体验并增强玩家的沉浸感。然而，由于在创新性、可靠性和资源分配等方面的优先级差异，学术原型常与实际应用脱节。本文通过系统性地概述当前两个领域中的PMG技术，提出一个双维度分类法，以弥合研究与应用之间的鸿沟。通过比较分析，本研究识别出算法实现、音乐质量与游戏集成方面的关键研究挑战。最后，本文展望了未来研究方向，强调面向任务和情境感知的设计、更全面的质量评估方法以及改进的研究工具集成，旨在为寻求在游戏场景中推进PMG的开发者、作曲家和研究者提供可操作的见解。