Patch-Based Convolutional Neural Network for the Writer Classification Problem in Music Score Images

Abstract

The Writer Identification Problem has been largely studied in the field of image processing. Music score writer identification is a particular type of the problem that requires identifying the writer of a music score, which is a complex task even for musicologists. Addressing this issue, this paper presents a novel Deep Learning approach based on a Convolutional Neural Network (CNN) for classifying music score images according to their writer. The classification is accomplished by dividing a music score image into patches that are fed to the CNN, which provides classification results for each patch. A voting system is then applied to obtain the final prediction of the model. This approach allows to learn local features of each music score in order to improve the final classification result. Results show that the proposed approach allows to obtain satisfactory results for the dataset used in this work, reaching 84%, 94% and 98% for the top-1, top-3 and top-5 accuracies, respectively.

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“…Patch based CNN was specifically used in the Music score images [34]. CNN used in the proposed system consists of three convolutional layers which takes in the input.…”