Please use this identifier to cite or link to this item: https://doi.org/10.1017/ATSIP.2018.10
Title: A technical framework for automatic perceptual evaluation of singing quality
Authors: Gupta C. 
Li H. 
Wang Y. 
Keywords: Automatic Evaluation
Human Perception
Perceptual Evaluation of Singing Quality
Singing Vocal
Issue Date: 2018
Publisher: Cambridge University Press
Citation: Gupta C., Li H., Wang Y. (2018). A technical framework for automatic perceptual evaluation of singing quality. APSIPA Transactions on Signal and Information Processing 7 : e10. ScholarBank@NUS Repository. https://doi.org/10.1017/ATSIP.2018.10
Abstract: Human experts evaluate singing quality based on many perceptual parameters such as intonation, rhythm, and vibrato, with reference to music theory. We proposed previously the Perceptual Evaluation of Singing Quality (PESnQ) framework that incorporated acoustic features related to these perceptual parameters in combination with the cognitive modeling concept of the telecommunication standard Perceptual Evaluation of Speech Quality to evaluate singing quality. In this study, we present further the study of the PESnQ framework to approximate the human judgments. First, we find that a linear combination of the individual perceptual parameter human scores can predict their overall singing quality judgment. This provides us with a human parametric judgment equation. Next, the prediction of the individual perceptual parameter scores from the PESnQ acoustic features show a high correlation with the respective human scores, which means more meaningful feedback to learners. Finally, we compare the performance of early fusion and late fusion of the acoustic features in predicting the overall human scores. We find that the late fusion method is superior to that of the early fusion method. This work underlines the importance of modeling human perception in automatic singing quality assessment.
Source Title: APSIPA Transactions on Signal and Information Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/152210
ISSN: 20487703
DOI: 10.1017/ATSIP.2018.10
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