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16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
Open AccessArticle

State-of-the-Art Model for Music Object Recognition with Deep Learning

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2645; https://doi.org/10.3390/app9132645
Received: 23 May 2019 / Revised: 11 June 2019 / Accepted: 26 June 2019 / Published: 29 June 2019
(This article belongs to the Special Issue Sound and Music Computing -- Music and Interaction)
Optical music recognition (OMR) is an area in music information retrieval. Music object detection is a key part of the OMR pipeline. Notes are used to record pitch and duration and have semantic information. Therefore, note recognition is the core and key aspect of music score recognition. This paper proposes an end-to-end detection model based on a deep convolutional neural network and feature fusion. This model is able to directly process the entire image and then output the symbol categories and the pitch and duration of notes. We show a state-of-the-art recognition model for general music symbols which can get 0.92 duration accurary and 0.96 pitch accuracy . View Full-Text
Keywords: optical music recognition; deep learning; object detection; note recognition optical music recognition; deep learning; object detection; note recognition
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Huang, Z.; Jia, X.; Guo, Y. State-of-the-Art Model for Music Object Recognition with Deep Learning. Appl. Sci. 2019, 9, 2645.

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