Judgments of music performance quality are commonly employed in music practice, education, and research. However, previous studies have demonstrated the limited reliability of such judgments, and there is now evidence that extraneous visual, social, and other “non-musical” features can unduly influence them. The present study employed continuous measurement techniques to examine how the process of forming a music quality judgment is affected by the manipulation of temporally specific visual cues. Video footage comprising an appropriate stage entrance and error-free performance served as the standard condition (Video 1). This footage was manipulated to provide four additional conditions, each identical save for a single variation: an inappropriate stage entrance (Video 2); the presence of an aural performance error midway through the piece (Video 3); the same error accompanied by a negative facial reaction by the performer (Video 4); the facial reaction with no corresponding aural error (Video 5). The participants were 53 musicians and 52 non-musicians (N = 105) who individually assessed the performance quality of one of the five randomly assigned videos via a digital continuous measurement interface and headphones. The results showed that participants viewing the “inappropriate” stage entrance made judgments significantly more quickly than those viewing the “appropriate” entrance, and while the poor entrance caused significantly lower initial scores among those with musical training, the effect did not persist long into the performance. The aural error caused an immediate drop in quality judgments that persisted to a lower final score only when accompanied by the frustrated facial expression from the pianist; the performance error alone caused a temporary drop only in the musicians' ratings, and the negative facial reaction alone caused no reaction regardless of participants' musical experience. These findings demonstrate the importance of visual information in forming evaluative and aesthetic judgments in musical contexts and highlight how visual cues dynamically influence those judgments over time.

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The automatic assessment of music performance has become an area of increasing interest due to the growing number of technology-enhanced music learning systems. In most of these systems, the assessment of musical performance is based on pitch and onset accuracy, but very few pay attention to other important aspects of performance, such as sound quality or timbre. This is particularly true in violin education, where the quality of timbre plays a significant role in the assessment of musical performances. However, obtaining quantifiable criteria for the assessment of timbre quality is challenging, as it relies on consensus among the subjective interpretations of experts. We present an approach to assess the quality of timbre in violin performances using machine learning techniques. We collected audio recordings of several tone qualities and performed perceptual tests to find correlations among different timbre dimensions. We processed the audio recordings to extract acoustic features for training tone-quality models. Correlations among the extracted features were analyzed and feature information for discriminating different timbre qualities were investigated. A real-time feedback system designed for pedagogical use was implemented in which users can train their own timbre models to assess and receive feedback on their performances.