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  4. Lyrics-Conditioned Neural Melody Generation
 
conference paper

Lyrics-Conditioned Neural Melody Generation

Yu, Yi
•
Harscoet, Florian
•
Canales, Simon  
Show more
January 1, 2020
Multimedia Modeling (Mmm 2020), Pt Ii
26th International Conference on MultiMedia Modeling (MMM)

Generating melody from lyrics to compose a song has been a very interesting research topic in the area of artificial intelligence and music, which tries to predict generative music relationship between lyrics and melody. In this demonstration paper, by exploiting a large music dataset with 12,197 pairs of English lyrics and melodies, we develop a lyrics-conditioned AI neural melody generation system that consists of three components: lyrics encoder network, melody generation network, and MIDI sequence tuner. Most importantly, a Long Short-Term Memory (LSTM)-based melody generator conditioned on lyrics, is trained by applying a generative adversarial network (GAN), to generate a pleasing and meaningful melody matching the given lyrics. Our demonstration illustrates the effectiveness of the proposed melody generation system.

  • Details
  • Metrics
Type
conference paper
DOI
10.1007/978-3-030-37734-2_58
Web of Science ID

WOS:000611566100058

Author(s)
Yu, Yi
Harscoet, Florian
Canales, Simon  
Reddy, Gurunath M.
Tang, Suhua
Jiang, Junjun
Date Issued

2020-01-01

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Publisher place

Cham

Published in
Multimedia Modeling (Mmm 2020), Pt Ii
ISBN of the book

978-3-030-37734-2

978-3-030-37733-5

Series title/Series vol.

Lecture Notes in Computer Science

Volume

11962

Start page

709

End page

714

Subjects

Computer Science, Artificial Intelligence

•

Computer Science, Software Engineering

•

Imaging Science & Photographic Technology

•

Computer Science

•

lyrics-conditioned melody generation

•

long short-term memory

•

generative adversarial network

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS5  
Event nameEvent placeEvent date
26th International Conference on MultiMedia Modeling (MMM)

Daejeon, SOUTH KOREA

Jan 05-08, 2020

Available on Infoscience
March 26, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/176747
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