Automatic Evaluation of Aspects of Performance and Scheduling in Playing the Piano

There is a growing trend to teach playing an instrument such as a piano at home using an automated system. A key component of such systems is the ability to rate performance of the learner in order to provide feedback and select appropriate exercises. In this study, we expand on previous works that have developed automatic evaluation systems for an overall grade by also providing predictions for specific aspects of performance: pitch, rhythm, tempo, and articulation & dynamics, as well as scheduling what is an appropriate next task. We describe how a set of salient features is extracted by comparing MIDI performance data of three piano players to an ideal performance, how the features used for evaluation are selected, and evaluate using linear regression how well the selected features are able to predict the mean scores given by a group of domain experts (piano teachers). Relatively good R2 scores (0.54 to 0.68) are achieved using a small number of features (2 - 4). Such automatic evaluation of different aspects of performance can be used as a part of an automatic learning system, and to help provide learners with detailed feedback on their performance.

In 30th ACM Conference on User Modeling, Adaptation and Personalization