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

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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%.

原文English
主出版物標題2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面285-290
頁數6
ISBN(電子)9781538605608
DOIs
出版狀態Published - 2017 9月 5
事件2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017 - Hong Kong, Hong Kong
持續時間: 2017 7月 102017 7月 14

出版系列

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

Other

Other2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
國家/地區Hong Kong
城市Hong Kong
期間17-07-1017-07-14

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 媒體技術

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