A symbol-level melody completion based on a convolutional neural network with generative adversarial learning

Kosuke Nakamura, Takashi Nose, Yuya Chiba, Akinori Ito

研究成果: Article査読

抄録

In this paper, we deal with melody completion, a technique which smoothly completes partially-masked melodies. 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 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. From the experimental result, it was confirmed that the network could generate original melody as a completion result and the quality of the generated melody was not significantly worse than the result of a simple example-based melody completion method.

本文言語English
ページ(範囲)248-257
ページ数10
ジャーナルJournal of information processing
28
DOI
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • Computer Science(all)

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