Inco-gan: Variable-length music generation method based on inception model-based conditional gan

Shuyu Li, Yunsick Sung

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Deep learning has made significant progress in the field of automatic music generation. At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. First, the length of the music must be determined artificially prior to generation. Second, although the convolutional neural network (CNN) is unexpectedly superior to the recurrent neural network (RNN), CNN still has several disadvantages. This paper proposes a conditional generative adversarial network approach using an inception model (INCO-GAN), which enables the generation of complete variable-length music automatically. By adding a time distribution layer that considers sequential data, CNN considers the time relationship in a manner similar to RNN. In addition, the inception model obtains richer features, which improves the quality of the generated music. In experiments conducted, the music generated by the proposed method and that by human composers were compared. High cosine similarity of up to 0.987 was achieved between the frequency vectors, indicating that the music generated by the proposed method is very similar to that created by a human composer.

Original languageEnglish
Article number387
Pages (from-to)1-16
Number of pages16
JournalMathematics
Volume9
Issue number4
DOIs
StatePublished - Feb 2021

Keywords

  • Conditional generative adversarial network
  • Convolutional neural network
  • Deep learning
  • Inception model
  • Music composition

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