Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts

TL;DR

This paper introduces a novel cross-modal style transfer method that converts music into visual art styles by leveraging semantic links between music features and visual representations, enabling era-specific artistic outputs.

Contribution

It proposes a two-step framework combining music visualization with style transfer, utilizing a conditional GAN and semantic era labels to generate era-specific visual styles from music.

Findings

The framework successfully generates diverse visual styles from music.

Era labels improve perceptual quality and semantic consistency.

Experiments on a new dataset demonstrate the method's effectiveness.

Abstract

Music-to-visual style transfer is a challenging yet important cross-modal learning problem in the practice of creativity. Its major difference from the traditional image style transfer problem is that the style information is provided by music rather than images. Assuming that musical features can be properly mapped to visual contents through semantic links between the two domains, we solve the music-to-visual style transfer problem in two steps: music visualization and style transfer. The music visualization network utilizes an encoder-generator architecture with a conditional generative adversarial network to generate image-based music representations from music data. This network is integrated with an image style transfer method to accomplish the style transfer process. Experiments are conducted on WikiArt-IMSLP, a newly compiled dataset including Western music recordings and paintings listed by decades. By utilizing such a label to learn the semantic connection between paintings and music, we demonstrate that the proposed framework can generate diverse image style representations from a music piece, and these representations can unveil certain art forms of the same era. Subjective testing results also emphasize the role of the era label in improving the perceptual quality on the compatibility between music and visual content.