用AI自动识别歌唱错误，助力音乐教学改进
Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy
- 基于教师学生同步录音数据，构建错误标注框架
- 深度学习模型比传统规则方法检测准确率更高
- 适合音乐教育科技、智能辅导系统开发者参考
机器学习在音频分析中的进步为技术赋能的音乐教育开辟了新可能。本文提出一个面向音乐教学的自动歌唱错误检测框架，并构建了一个全新整理的数据集。该数据集包含教师与学习者同步的声乐录音，且对学习者犯错类型进行了标注。利用该数据集，我们开发并对比了多种深度学习模型的错误检测性能。为评估检测系统的有效性，提出一种新的评价方法。实验表明，基于学习的方法优于基于规则的方法。对错误模式的系统研究及跨教师对比揭示了音乐教学中的深层规律，可服务于各类音乐应用。本工作为音乐教学研究指明了新方向。代码与数据集均已公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。