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An Evaluation of Score Descriptors Combined with Non-linear Models of Expressive Dynamics in Music

机译:评分评分描述符与音乐中的表达动态的非线性模型相结合

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Expressive interpretation forms an important but complex aspect of music, in particular in certain forms of classical music. Modeling the relation between musical expression and structural aspects of the score being performed, is an ongoing line of research. Prior work has shown that some simple numerical descriptors of the score (capturing dynamics annotations and pitch) are effective for predicting expressive dynamics in classical piano performances. Nevertheless, the features have only been tested in a very simple linear regression model. In this work, we explore the potential of a non-linear model for predicting expressive dynamics. Using a set of descriptors that capture different types of structure in the musical score, we compare the predictive accuracies of linear and non-linear models. We show that, in addition to being (slightly) more accurate, non-linear models can better describe certain interactions between numerical descriptors than linear models.
机译:表达解释形成了音乐的重要但复杂的方面,特别是在某些形式的古典音乐中。建模音乐表达与正在进行得分的结构方面的关系,是一个持续的研究线。事先工作表明,分数的一些简单数值描述符(捕获动力学注释和间距)是有效地预测古典钢琴表演中的表达动态。然而,只有在一个非常简单的线性回归模型中只测试了该功能。在这项工作中,我们探讨了预测表达动态的非线性模型的潜力。使用一组描述符号在乐谱中捕获不同类型的结构,我们比较线性和非线性模型的预测精度。我们表明,除了(略微)更准确,非线性模型可以更好地描述数值描述符之间的某些交互,而不是线性模型。

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