How does the teacher rate? Observations from the NeuroPiano dataset

Fuente: arXiv
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Autori principali: Zhang, Huan, Cheung, Vincent, Nishioka, Hayato, Dixon, Simon, Furuya, Shinichi
Natura: Preprint
Pubblicazione: 2024
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author Zhang, Huan
Cheung, Vincent
Nishioka, Hayato
Dixon, Simon
Furuya, Shinichi
author_facet Zhang, Huan
Cheung, Vincent
Nishioka, Hayato
Dixon, Simon
Furuya, Shinichi
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_03139
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle How does the teacher rate? Observations from the NeuroPiano dataset
Zhang, Huan
Cheung, Vincent
Nishioka, Hayato
Dixon, Simon
Furuya, Shinichi
Audio and Speech Processing
Sound
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.
title How does the teacher rate? Observations from the NeuroPiano dataset
topic Audio and Speech Processing
Sound
url https://arxiv.org/abs/2410.03139