This article presents a machine-learning technique to analyze and produce statistical patterns in rhythm through real-time observation of human musicians. Here, timbre is considered an integral part of rhythm, as might be exemplified by hand-drum music. Moreover, this article considers challenges (such as mechanical timing delays, that are negligible in digitally synthesized music) that arise when the algorithm is executed on percussion robots. The algorithm's performance is analyzed in a variety of contexts, such as learning specific rhythms, learning a corpus of rhythms, responding to signal rhythms that signal musical transitions, improvising in different ways with a human partner, and matching the meter and the “syncopicity” of improvised music.

中文翻译：

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打击乐机器人音色节奏的交互式学习

本文介绍了一种机器学习技术，可通过对人类音乐家的实时观察来分析和生成节奏中的统计模式。在这里，音色被认为是节奏的一个组成部分，例如手鼓音乐。此外，本文还考虑了在打击乐机器人上执行算法时出现的挑战（例如机械时间延迟，在数字合成音乐中可以忽略不计）。该算法的性能在各种环境中进行分析，例如学习特定节奏、学习节奏语料库、响应标志着音乐过渡的信号节奏、与人类合作伙伴以不同方式即兴创作以及匹配节拍和“晕厥”即兴音乐。