Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-like patterning: the placement of game objects in relation to each other to form congruent patterns based on events in the song. Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.

翻译：通过机器学习从歌曲中产生节奏游戏图表是近年来人们越来越感兴趣的问题。然而,所有现有系统都在努力复制人样的图案:根据歌曲中的事件,将游戏对象相对地放置在一起,形成一致的模式。 模式是高质量节奏游戏内容的关键标识,被视为人类排名的一个必要组成部分。 我们为图表生成建立了一个新方法,该方法产生的图表比以往工作中的图表更加一致,更像人类。