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Body Movement Generation for Expressive Violin Performance Applying Neural Networks

机译:运用神经网络产生小提琴演奏的身体运动

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Generating body movements based on given music audio recordings is an emerging research topic. This problem remains challenging particularly for string instruments, considering the fact that the relationship between the musical note sequences and the body movement sequences in string instruments does not have an one-to-one correspondence and is highly context-dependent. In this paper, we take a divide-and-rule approach to tackle the multifaceted characteristics of musical movement, and propose a framework for generating violinists’ body movements. Both objective and subjective evaluations show that the proposed framework improves the stability as well as the perceptual quality of the generation outputs by using the task-specific models for bowing and expressive movement. To the best of our knowledge, this work represents the first attempt to generate violinists’ body movements considering music expression.
机译:基于给定的音乐录音来产生身体运动是一个新兴的研究主题。考虑到弦乐器中的音符序列与身体运动序列之间的关系不具有一一对应关系并且高度依赖于上下文这一事实,这个问题对于弦乐器尤其仍然具有挑战性。在本文中,我们采用分而治之的方法来解决音乐运动的多方面特征,并提出了一个产生小提琴手身体运动的框架。客观评估和主观评估都表明,所提出的框架通过使用特定于任务的鞠躬和表情移动模型提高了发电输出的稳定性以及感知质量。据我们所知,这项工作代表了在考虑音乐表达的情况下产生小提琴手的身体动作的首次尝试。

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