TY - GEN
T1 - DeepSheet
T2 - 2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
AU - Hsu, Yu Lun
AU - Lin, Chi Po
AU - Lin, Bo Chen
AU - Kuo, Hsu Chan
AU - Cheng, Wen Huang
AU - Hu, Min Chun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/5
Y1 - 2017/9/5
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85031665400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031665400&partnerID=8YFLogxK
U2 - 10.1109/ICMEW.2017.8026272
DO - 10.1109/ICMEW.2017.8026272
M3 - Conference contribution
AN - SCOPUS:85031665400
T3 - 2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
SP - 285
EP - 290
BT - 2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 July 2017 through 14 July 2017
ER -