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Open AccessArticle

Assessment of Student Music Performances Using Deep Neural Networks

Center for Music Technology, Georgia Institute of Technology, Atlanta, GA 30318, USA
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2018, 8(4), 507; https://doi.org/10.3390/app8040507
Received: 28 February 2018 / Revised: 19 March 2018 / Accepted: 22 March 2018 / Published: 27 March 2018
(This article belongs to the Special Issue Digital Audio and Image Processing with Focus on Music Research)
Music performance assessment is a highly subjective task often relying on experts to gauge both the technical and aesthetic aspects of the performance from the audio signal. This article explores the task of building computational models for music performance assessment, i.e., analyzing an audio recording of a performance and rating it along several criteria such as musicality, note accuracy, etc. Much of the earlier work in this area has been centered around using hand-crafted features intended to capture relevant aspects of a performance. However, such features are based on our limited understanding of music perception and may not be optimal. In this article, we propose using Deep Neural Networks (DNNs) for the task and compare their performance against a baseline model using standard and hand-crafted features. We show that, using input representations at different levels of abstraction, DNNs can outperform the baseline models across all assessment criteria. In addition, we use model analysis techniques to further explain the model predictions in an attempt to gain useful insights into the assessment process. The results demonstrate the potential of using supervised feature learning techniques to better characterize music performances. View Full-Text
Keywords: music performance assessment; deep learning; deep neural networks; DNN; music information retrieval; MIR; music informatics; music education; music learning music performance assessment; deep learning; deep neural networks; DNN; music information retrieval; MIR; music informatics; music education; music learning
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MDPI and ACS Style

Pati, K.A.; Gururani, S.; Lerch, A. Assessment of Student Music Performances Using Deep Neural Networks. Appl. Sci. 2018, 8, 507. https://doi.org/10.3390/app8040507

AMA Style

Pati KA, Gururani S, Lerch A. Assessment of Student Music Performances Using Deep Neural Networks. Applied Sciences. 2018; 8(4):507. https://doi.org/10.3390/app8040507

Chicago/Turabian Style

Pati, Kumar A.; Gururani, Siddharth; Lerch, Alexander. 2018. "Assessment of Student Music Performances Using Deep Neural Networks" Appl. Sci. 8, no. 4: 507. https://doi.org/10.3390/app8040507

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