Piano is one of the most popular instruments among music learners. Technologies to evaluate piano performances have been researched and developed in recent years rapidly, including data driven methods using
machine learning. Despite the demand from people and speed of the development, there are still gaps between
the methods and the pedagogical setup for real use case scenarios due to lack of accuracy of methods, insufficient amount of training data or the biases in training machine learning models, ...
Piano is one of the most popular instruments among music learners. Technologies to evaluate piano performances have been researched and developed in recent years rapidly, including data driven methods using
machine learning. Despite the demand from people and speed of the development, there are still gaps between
the methods and the pedagogical setup for real use case scenarios due to lack of accuracy of methods, insufficient amount of training data or the biases in training machine learning models, ignoring actual use case of
the technology and such. In this paper, we first propose a feedback approach in piano performance education
and review methods for Automated Piano Performance Assessment (APPA). After that, we discuss about gaps
between a feedback approach and current methods, emphasizing their music education application. As a future
work we propose a potential approach to overcome the gaps.
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