DeepSheet: A sheet music generator based on deep learning

Yu Lun Hsu, Chi Po Lin, Bo Chen Lin, Hsu Chan Kuo, Wen Huang Cheng, Min Chun Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sheet music has long been regarded as one of the most effective medias for musicians, music players, and amateurs to communicate with each other. It is also an intuitive way for non-professionals to learn how to play a musical instrument or sing a song. However, not all composers have willingness to share their own sheet music, especially those protected by strict copyright regulations. For amateurs or novice musicians without the capability to distinguish the chords by their own ears, it would be difficult for them to enjoy the happiness of playing other's music, or singing songs. In order to provide the beginners with an effective way to play music or sing songs without a given sheet music, we developed a sheet music generator-'DeepSheet', which was engineered based on deep learning techniques. Basically, the DeepSheet system is mainly composed of three distinctive components as follows: 1. voice separation of the audio song, 2. chord estimation of the background music, and 3. alignment of lyrics and music beats. The experimental results drawn from more than 150 songs of the Beatles and Queen indicated that the newly developed DeepSheet system has the capability and sensibility to generate sheet music with accuracy of approximately 76%.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-290
Number of pages6
ISBN (Electronic)9781538605608
DOIs
Publication statusPublished - 2017 Sep 5
Event2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017 - Hong Kong, Hong Kong
Duration: 2017 Jul 102017 Jul 14

Publication series

Name2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017

Other

Other2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
CountryHong Kong
CityHong Kong
Period17-07-1017-07-14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Media Technology

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