ABSTRACT Machine learning has been used to accurately classify musical genre using features derived from audio signals. Musical genre, as well as lower-level audio features of music, have also been shown to influence music-induced movement, however, the degree to which such movements are genre-specific has not been explored. The current paper addresses this using motion capture data from participants dancing freely to eight genres. Using a Support Vector Machine model, data were classified by genre and by individual dancer. Against expectations, individual classification was notably more accurate than genre classification. Results are discussed in terms of embodied cognition and culture.

中文翻译：

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跟着自己的鼓起舞：使用机器学习从动作捕捉中识别音乐流派和个人舞者

摘要 机器学习已被用于使用源自音频信号的特征对音乐类型进行准确分类。音乐流派以及音乐的低级音频特征也已被证明会影响音乐引起的运动，但是，尚未探索此类运动特定于流派的程度。当前的论文使用来自参与者自由跳舞到八种类型的动作捕捉数据来解决这个问题。使用支持向量机模型，数据按流派和个人舞者分类。与预期相反，个人分类明显比流派分类更准确。结果在具身认知和文化方面进行了讨论。