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.

Authors: Vidwans, Amruta; Gururani, Siddharth; Wu, Chih-Wei; Subramanian, Vinod; Swaminathan, Rupak Vignesh; Lerch, Alexander
Affiliation: Georgia Institute of Technology, Atlanta, GA, USA
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: 3-3
Publication Date: June 13, 2017
Subject: Pitch Tracking

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:
Username:
Password:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.