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Paper Abstract and Keywords
Presentation 0000-00-00 00:00
Music generation that stops at a specified time
Kaku Oeda, Heitoh Zen (Chiba Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we aim to automatically generate jingles. Jingles are short songs that are inserted into scene changes in video works. By applying a RNN-based generative model that learns MIDI, We propose a new generation model that finishes in a specified time by introducing the condition of “Progress” of a music. We conducted an experiment using learning with open data, and compared how the generated music changes depending on the presence or absence of progress conditioning. As a result, it was confirmed that the proposed method produced a song that ended more naturally.
Keyword (in Japanese) (See Japanese page) 
(in English) music generation / deep learning / RNN / / / / /  
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Conference Information
Committee HCGSYMPO  
Conference Date 2020-12-15 - 2020-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
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Paper Information
Registration To HCGSYMPO 
Conference Code 2020-12-HCGSYMPO 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Music generation that stops at a specified time 
Sub Title (in English)  
Keyword(1) music generation  
Keyword(2) deep learning  
Keyword(3) RNN  
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1st Author's Name Kaku Oeda  
1st Author's Affiliation Chiba Univ. Graduate School of Sci. and Eng., Div. of Math. and Infomatics (Chiba Univ.)
2nd Author's Name Heitoh Zen  
2nd Author's Affiliation Chiba Univ., Inst. of Management and Info. Tech. (Chiba Univ.)
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