The advancement of machine learning in audio analysis has opened new possibilities for technology-enhanced music education. This paper introduces a framework for automatic singing mistake detection in the context of music pedagogy, supported by a newly curated dataset. The dataset comprises synchronized teacher learner vocal recordings, with annotations marking different types of mistakes made by learners. Using this dataset, we develop different deep learning models for mistake detection and benchmark them. To compare the efficacy of mistake detection systems, a new evaluation methodology is proposed. Experiments indicate that the proposed learning-based methods are superior to rule-based methods. A systematic study of errors and a cross-teacher study reveal insights into music pedagogy that can be utilised for various music applications. This work sets out new directions of research in music pedagogy. The codes and dataset are publicly available.

翻译：音频分析中机器学习的进步为技术增强型音乐教育开辟了新的可能性。本文在全新构建的数据集支持下，提出了一种面向音乐教学的歌唱错误自动检测框架。该数据集包含同步录制的教师与学生演唱音频，并标注了学生所犯的不同类型错误。利用此数据集，我们开发了多种用于错误检测的深度学习模型并对其进行了基准测试。为比较不同错误检测系统的效能，本文提出了一种新的评估方法。实验表明，所提出的基于学习的方法优于基于规则的方法。对错误的系统性研究以及跨教师研究揭示了音乐教学中的深刻见解，这些见解可应用于多种音乐场景。本工作为音乐教学研究设定了新的方向。相关代码与数据集均已公开。