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- W3130147800 abstract "Technology improvement is rapidly changing. This impacts many fields, including the music field. Technology has helped the music field to be recognised in machine understanding. This field is called optical music recognition (OMR), a computer vision enabler in music. With OMR, we can define the position and music notation in music note. We propose a deep learning and convolutional neural network (CNN) approach to recognise a music position in music note. Music note position in staff is one of the keys to achieve pitch recognition. While we have music clef, key signature, and note position in staff, we can give machines the understanding of a note pitch. This experiment can bring and broaden the experiments in recognising music pitch, which take music note image as an input and position as the output. We use our own dataset and use CNN in experiments. This note position recognition experiment achieved 80% accuracy." @default.
- W3130147800 created "2021-03-01" @default.
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- W3130147800 date "2021-01-01" @default.
- W3130147800 modified "2023-09-24" @default.
- W3130147800 title "Music Note Position Recognition in Optical Music Recognition using Convolutional Neural Network" @default.
- W3130147800 doi "https://doi.org/10.1504/ijart.2021.10035633" @default.
- W3130147800 hasPublicationYear "2021" @default.
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