The qualitative assessment of music performances is a task 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 U.S. All-State auditions or other competitions. A model that automatically generates assessments from audio data allows for objective assessments and enables musically intelligent computer-assisted practice sessions for students learning an instrument.

This research aims to characterize the performance with well-established and custom-designed audio features, model expert assessments of student performances, and predict the assessment of unknown audio recordings. Results could lead to more general software music tutoring systems that do not require score information for the assessment of student music performances.

Publications

• Gururani, S.; Pati, K.A.; Wu, C.-W.; Lerch, A., Analysis of Objective Descriptors for Music Performance Assessment, In Proceedings of the International Conference on Music Perception and Cognition (ICMPC), Toronto, Ontario, Canada, 2018.
• Pati, K.A.; Gururani, S.; Lerch, A., Assessment of Student Music Performances Using Deep Neural Networks, Applied Sciences, 8 (4), pp. 507, 2018.
• Wu, C.-W.; Lerch, A., Learned Features for the Assessment of Percussive Music Performances, Proceedings of the International Conference on Semantic Computing (ICSC), IEEE, Laguna Hills, 2018.
• Vidwans, A., Gururani, S., Wu, C.-W., Subramanian, V., Swaminathan, R. V., Lerch, A., Objective descriptors for the assessment of student music performances, Proceedings of the AES Conference on Semantic Audio, Audio Engineering Society (AES), Erlangen, 2017
• Wu, C.-W.; Gururani, S.; Laguna, C.; Pati, A.; Vidwans, A.; Lerch, A., Towards the Objective Assessment of Music Performances, Proceedings of the International Conference on Music Perception and Cognition (ICMPC), San Francisco, 2016