How does the teacher rate? Observations from the NeuroPiano dataset
Audio and Speech Processing
2024-10-07 v1 Sound
Abstract
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.
Keywords
Cite
@article{arxiv.2410.03139,
title = {How does the teacher rate? Observations from the NeuroPiano dataset},
author = {Huan Zhang and Vincent Cheung and Hayato Nishioka and Simon Dixon and Shinichi Furuya},
journal= {arXiv preprint arXiv:2410.03139},
year = {2024}
}