分析104段钢琴演奏数据，揭示教师评分规律与音频特征关系
How does the teacher rate? Observations from the NeuroPiano dataset
- 收集104段学生演奏与2255条专业教师反馈
- 12项评估问题中教师评分一致性达0.72以上
- 发现音频特征可预测教师评分，适合教育科技研究者
本文对NeuroPiano数据集进行深入分析，该数据集包含104个学生钢琴演奏的音频记录，以及来自专业钢琴教师的2255条文本反馈和评分。研究从统计角度概述了数据集，重点关注12个关于演奏质量评估问题的标注标准化程度和评分者间一致性。同时，通过机器学习方法探索音频特征与教师评分之间的预测关系，并为后续文本分析提供注释支持。
原文摘要 · Abstract (English)
This paper provides a detailed analysis of the NeuroPiano dataset, which comprise 104 audio recordings of student piano performances accompanied with 2255 textual feedback and ratings given by professional pianists. We offer a statistical overview of the dataset, focusing on the standardization of annotations and inter-annotator agreement across 12 evaluative questions concerning performance quality. We also explore the predictive relationship between audio features and teacher ratings via machine learning, as well as annotations provided for text analysis of the responses.
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