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A. Vidwans, S. Gururani, C. Wu, V. Subramanian, RU. VI. Swaminathan, and A. Lerch, "Objective Descriptors for the Assessment of Student Music Performances," Paper 3-3, (2017 June.). doi: A. Vidwans, S. Gururani, C. Wu, V. Subramanian, RU. VI. Swaminathan, and A. Lerch, "Objective Descriptors for the Assessment of Student Music Performances," Paper 3-3, (2017 June.). doi:
Abstract: 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.

@article{vidwans2017objective,
author={vidwans, amruta and gururani, siddharth and wu, chih-wei and subramanian, vinod and swaminathan, rupak vignesh and lerch, alexander},
journal={journal of the audio engineering society},
title={objective descriptors for the assessment of student music performances},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},} @article{vidwans2017objective,
author={vidwans, amruta and gururani, siddharth and wu, chih-wei and subramanian, vinod and swaminathan, rupak vignesh and lerch, alexander},
journal={journal of the audio engineering society},
title={objective descriptors for the assessment of student music performances},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},
abstract={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.},}

TY - paper
TI - Objective Descriptors for the Assessment of Student Music Performances
SP - EP -
AU - Vidwans, Amruta
AU - Gururani, Siddharth
AU - Wu, Chih-Wei
AU - Subramanian, Vinod
AU - Swaminathan, Rupak Vignesh
AU - Lerch, Alexander
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017 TY - paper
TI - Objective Descriptors for the Assessment of Student Music Performances
SP - EP -
AU - Vidwans, Amruta
AU - Gururani, Siddharth
AU - Wu, Chih-Wei
AU - Subramanian, Vinod
AU - Swaminathan, Rupak Vignesh
AU - Lerch, Alexander
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017
AB - 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.