Objective Descriptors For The Assessment Of Student Music Performances

Assessment of students' music performances is a subjective task that requires the judgment of technical correctness as well as aesthetic properties. A computational model automatically evaluating music performance based on objective measurements could ensure consistent and reproducible assessments for, e.g., automatic music tutoring systems. In this study, we investigate the effectiveness of various audio descriptors for assessing 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 that human assessments can be modeled to a certain degree, however, the generality of the model still needs further investigation.