Visualyre: multimodal album art generation for independent musicians

Gamar Azuaje, Kongmeng Liew, Elena V. Epure, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki

Abstract: Album art often reflects the trends and themes of the songs in a given collection, and even the identities of the musicians who produced it. It therefore plays a central role in fomenting a potential listener’s first impression of the work. As such, musicians strive to find suitable images for this purpose, and those with limited financial resources or design skills may struggle to do so. Here, we report the development of Visualyre, a deep learning–based application that generates album art images from users’ song lyrics and audio files. This tool relies on generative adversarial network models to generate images from textual input (lyrics) and style transfer models to adjust the image according to the mood of the audio. We then report the results of a user study involving 35 amateur and independent musicians who tested the system. Results suggest that Visualyre was generally well received and largely effective in its intended purpose: providing musicians with a resource for generating their own album art.