Abstract

The qualitative assessment of music performances is a task that is influenced by technical correctness, deviations from established performance standards, and aesthetic judgment. Despite its inherently subjective nature, a quantitative overall assessment is often desired, as exemplified by US all-state auditions or other competitions. A model that automatically generates assessments from the audio data would allow for objective assessments and enable musically intelligent computer-assisted practice sessions for students learning an instrument. While existing systems are already able to provide similar basic functionality, they rely on the musical score as prior knowledge. In this paper, we present a score-independent system for assessing student instrument performances based on audio recordings. This system aims to characterize the performance with both well-established and custom-designed audio features, model expert assessments of student performances, and predict the assessment of unknown audio recordings. The results imply the viability of modeling human assessment with score-independent audio features. Results could lead towards more general software music tutoring systems that do not require score information for the assessment of student music performances.