首页> 外文会议>International Conference on Artificial Intelligence in Music, Sound, Art and Design;Evostar Conferences >Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction
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Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

机译:Chord Embeddings:分析他们捕获的内容和艺术家属性预测的作用及其作用

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Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating chord embeddings, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.
机译:自然语言处理方法已应用于各种音乐研究,绘制了音乐和语言之间的连接。 在本文中,我们通过调查和弦嵌入式来扩展这些方法,我们在两种案例研究中申请解决两个关键问题:(1)和弦嵌入的音乐信息捕获? 和(2)音乐应用程序如何受益于它们? 在我们的分析中,我们表明他们捕获了与音乐理论中描述的重要关系遵循的和弦之间的相似之处。 在第一种案例研究中,我们证明,在下一个和弦预测任务中使用Chord Embeddings,产生更符合经验丰富的音乐家的预测。 在第二个案例研究中,我们展示了使用与音乐风格测量学相关的任务中的表示的潜在好处。

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