Learning music is a challenging process that requires years of practice to master, either with lessons from a professional teacher or through self-teaching. While practicing, students are expected to self-evaluate their performance which may be difficult without timely feedback from a professional. Research into computer-assisted music instrument tutoring (CAMIT) attempts to address this through the use of emerging technologies. In this paper, we study CAMIT for mixed reality (MR) by developing MR:emin, an immersive MR music learning environment for the theremin, an electronic music instrument that is controlled without physical contact. MR:emin integrates a physical theremin with the immersive learning environment. To better understand the effectiveness of such environments, we perform a user study with MR:emin comparing traditional music learning with two virtual learning environments, an immersive one and a non-immersive one. In a between-groups study, 30 participants were trained to play a sequence of notes on the theremin using one of the three training environments. Results of our statistical analysis show that performance error during training is significantly smaller in the immersive MR environment. This does not necessarily lead to improved performance after training; analysis of post-training improvement indicates that immersive training results in the smallest amount of improvement. Participants, however, indicate that the MR:emin environment is more engaging and increases confidence during practice. We discuss potential factors leading to the decrease in learning and provide some environment guidelines to aid in the design of engaging immersive music learning environments.

中文翻译：

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用Theremin评估混合现实乐器学习的有效性

学习音乐是一个具有挑战性的过程，需要多年的实践才能掌握，要么通过专业老师的授课，要么通过自学。在练习过程中，学生需要自我评估他们的表现，如果没有专业人士的及时反馈，这可能会很困难。对计算机辅助乐器辅导（CAMIT）的研究试图通过使用新兴技术来解决这一问题。在本文中，我们通过开发MR：emin来研究混合现实（MR）的CAMIT，这是一种针对居民的沉浸式MR音乐学习环境，一种无需物理接触即可控制的电子乐器。MR：emin将物理Theremin与沉浸式学习环境集成在一起。为了更好地了解此类环境的有效性，我们使用MR：emin进行了一项用户研究，将传统音乐学习与两种虚拟学习环境（沉浸式学习环境和非沉浸式学习环境）进行了比较。在小组之间的研究中，使用三种培训环境中的一种，对30名参与者进行了训练，以在其上弹奏一系列音符。我们的统计分析结果表明，在沉浸式MR环境中，训练期间的性能误差明显较小。这并不一定会导致训练后的表现提高；训练后改善的分析表明，沉浸式训练导致的改善量最小。但是，参与者指出MR：emin环境在练习中更具吸引力，并增加了信心。我们讨论了导致学习减少的潜在因素，并提供了一些环境准则来帮助设计引人入胜的音乐学习环境。