Assessment of students' music performances is a subjective task that requires the judgment of technical correctness as well as aesthetic properties. A computational model that automatically evaluates music performance based on objective measurements is often desirable to ensure the consistency and reproducibility of these assessments, e.g., for automatic music tutoring systems. In this study, we investigate the effectiveness of various audio descriptors for assessing students’ performances. Specifically, three different sets of features, including a baseline set, score-independent features, and score-based features, are compared with respect to their efficiency in regression tasks. The results show human assessments can be modeled to a certain degree, however, the generality of the model still needs further investigation.
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