TY - GEN
T1 - Neural Melody Composition from Lyrics
AU - Bao, Hangbo
AU - Huang, Shaohan
AU - Wei, Furu
AU - Cui, Lei
AU - Wu, Yu
AU - Tan, Chuanqi
AU - Piao, Songhao
AU - Zhou, Ming
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this paper, we study a novel task that learns to compose music from natural language. Given the lyrics as input, we propose a melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously. More specifically, we develop the melody composition model based on the sequence-to-sequence framework. It consists of two neural encoders to encode the current lyrics and the context melody respectively, and a hierarchical decoder to jointly produce musical notes and the corresponding alignment. Experimental results on lyrics-melody pairs of 18,451 pop songs demonstrate the effectiveness of our proposed methods. In addition, we apply a singing voice synthesizer software to synthesize the “singing” of the lyrics and melodies for human evaluation. Results indicate that our generated melodies are more melodious and tuneful compared with the baseline method.
AB - In this paper, we study a novel task that learns to compose music from natural language. Given the lyrics as input, we propose a melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously. More specifically, we develop the melody composition model based on the sequence-to-sequence framework. It consists of two neural encoders to encode the current lyrics and the context melody respectively, and a hierarchical decoder to jointly produce musical notes and the corresponding alignment. Experimental results on lyrics-melody pairs of 18,451 pop songs demonstrate the effectiveness of our proposed methods. In addition, we apply a singing voice synthesizer software to synthesize the “singing” of the lyrics and melodies for human evaluation. Results indicate that our generated melodies are more melodious and tuneful compared with the baseline method.
KW - Conditional sequence generation
KW - Neural melody composition
UR - https://www.scopus.com/pages/publications/85075550524
U2 - 10.1007/978-3-030-32233-5_39
DO - 10.1007/978-3-030-32233-5_39
M3 - 会议稿件
AN - SCOPUS:85075550524
SN - 9783030322328
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 511
BT - Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings
A2 - Tang, Jie
A2 - Kan, Min-Yen
A2 - Zhao, Dongyan
A2 - Li, Sujian
A2 - Zan, Hongying
PB - Springer
T2 - 8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019
Y2 - 9 October 2019 through 14 October 2019
ER -