Melody completion based on convolutional neural networks and generative adversarial learning

Kosuke Nakamura, Takashi Nose, Yuya Chiba, Akinori Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we deal with melody completion, a technique which smoothly completes melodies that are partially masked. Melody completion can be used to help people compose or arrange pieces of music in several ways, such as editing existing melodies or connecting two other melodies. In recent years, various methods have been proposed for realizing high-quality completion via neural networks. Therefore, in this research, we examine a method of melody completion based on an image completion network. We represent melodies of a certain length as images and train a completion network to complete those images. The completion network consists of convolution layers and is trained in the framework of generative adversarial networks. We also consider chord progression from musical pieces as conditions.

Original languageEnglish
Title of host publicationRecent Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceeding of the Fourteenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
EditorsLakhmi C. Jain, Lakhmi C. Jain, Pei-Wei Tsai, Akinori Ito, Jeng-Shyang Pan, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages116-123
Number of pages8
ISBN (Print)9783030037475
DOIs
Publication statusPublished - 2019 Jan 1
Event14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018 - Sendai, Japan
Duration: 2018 Nov 262018 Nov 28

Publication series

NameSmart Innovation, Systems and Technologies
Volume110
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Other

Other14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018
CountryJapan
CitySendai
Period18/11/2618/11/28

Keywords

  • Automatic music composition
  • Convolutional neural networks
  • Generative adversarial networks
  • Melody completion

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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