  Over 60,000 songs are released on Spotify every day, and the competition for
the listener's attention is immense. In that regard, the importance of
captivating and inviting cover art cannot be underestimated, because it is
deeply entangled with a song's character and the artist's identity, and remains
one of the most important gateways to lead people to discover music. However,
designing cover art is a highly creative, lengthy and sometimes expensive
process that can be daunting, especially for non-professional artists. For this
reason, we propose a novel deep-learning framework to generate cover art guided
by audio features. Inspired by VQGAN-CLIP, our approach is highly flexible
because individual components can easily be replaced without the need for any
retraining. This paper outlines the architectural details of our models and
discusses the optimization challenges that emerge from them. More specifically,
we will exploit genetic algorithms to overcome bad local minima and adversarial
examples. We find that our framework can generate suitable cover art for most
genres, and that the visual features adapt themselves to audio feature changes.
Given these results, we believe that our framework paves the road for
extensions and more advanced applications in audio-guided visual generation
tasks.
