How does the teacher rate? Observations from the NeuroPiano dataset

Huan Zhang (Queen Mary University of London)*, Vincent K.M. Cheung (Sony Computer Science Laboratories, Inc.), Hayato Nishioka (Sony Computer Science Laboratories, Inc), Simon Dixon (Queen Mary University of London), Shinichi Furuya (Sony Computer Science Laboratories Inc.)

lbd-465-how-does-the

This paper will be presented both in person and virtually at the 12:15 PM - 12:45 PM PST session

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.