How does the teacher rate? Observations from the NeuroPiano dataset

Author

Zhang, Huan and Cheung, Vincent and Nishioka, Hayato and Dixon, Simon and Furuya, Shinichi

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.