Title

How can generative adversarial networks impact computer generated art? Insights from poetry to melody conversion

Document Type

Article

Source of Publication

International Journal of Information Management Data Insights

Publication Date

4-1-2022

Abstract

Recent advances in deep learning and generative adversarial networks (GANs), in particular, has enabled interesting applications including photorealistic image generation, image translation, and automatic caption generation. This has opened up possibilities for many cross-domain applications in computer generated arts and literature. Although there are existing software-based approaches for generating musical accompaniment of a given poetry, there are no existing implementation using GANs. This work proposes a novel poetry to melody generation conditioned on poem emotion using GANs. A dataset containing pairs of poetry and melody based on three emotion categories is introduced. Furthermore, various GAN architectures including SpecGAN and WaveGAN were explored for automatic melody synthesis for a given class of poetry. Conditional SpecGAN produced the best melodies according to quantitative metrics. Melodies produced by SpecGAN were evaluated by volunteers who deemed the quality to be above average.

ISSN

Publisher

Elsevier

Volume

2

Issue

1

First Page

100066

Last Page

100066

Disciplines

Computer Sciences

Keywords

Generative adversarial networks, Deep learning, Melody generation, Music generation, Artificial intelligence, Computer generated art

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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