Musicality-novelty generative adversarial nets for algorithmic composition

Gong Chen, Zhong Shenghua, Yan Liu, Xiang Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


Algorithmic composition, which enables computer to generate music like human composers, has lasting charm because it intends to approximate artistic creation, most mysterious part of human intelligence. To deliver both melodious and refreshing music, this paper proposes the Musicality-Novelty Generative Adversarial Nets for algorithmic composition. With the same generator, two adversarial nets alternately optimize the musicality and novelty of the machine-composed music. A new model called novelty game is presented to maximize the minimal distance between the machine-composed music sample and any human-composed music sample in the novelty space, where all well-known human composed music products are far from each other. We implement the proposed framework using three supervised CNNs with one for generator, one for musicality critic and one for novelty critic on the time-pitch feature space. Specifically, the novelty critic is implemented by Siamese neural networks with temporal alignment using dynamic time warping. We provide empirical validations by generating the music samples under various scenarios.

Original languageEnglish
Title of host publicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450356657
Publication statusPublished - 15 Oct 2018
Event26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
Duration: 22 Oct 201826 Oct 2018

Publication series

NameMM 2018 - Proceedings of the 2018 ACM Multimedia Conference


Conference26th ACM Multimedia conference, MM 2018
Country/TerritoryKorea, Republic of


  • Algorithmic composition
  • Generative adversarial nets
  • Music

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction


Dive into the research topics of 'Musicality-novelty generative adversarial nets for algorithmic composition'. Together they form a unique fingerprint.

Cite this