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Over 60,000 songs are released on Spotify every day, and the competition forthe listener's attention is immense. In that regard, the importance ofcaptivating and inviting cover art cannot be underestimated, because it isdeeply entangled with a song's character and the artist's identity, and remainsone of the most important gateways to lead people to discover music. However,designing cover art is a highly creative, lengthy and sometimes expensiveprocess that can be daunting, especially for non-professional artists. For thisreason, we propose a novel deep-learning framework to generate cover art guidedby audio features. Inspired by VQGAN-CLIP, our approach is highly flexiblebecause individual components can easily be replaced without the need for anyretraining. This paper outlines the architectural details of our models anddiscusses the optimization challenges that emerge from them. More specifically,we will exploit genetic algorithms to overcome bad local minima and adversarialexamples. We find that our framework can generate suitable cover art for mostgenres, and that the visual features adapt themselves to audio feature changes.Given these results, we believe that our framework paves the road forextensions and more advanced applications in audio-guided visual generationtasks.

Audio-guided Album Cover Art Generation with Genetic Algorithms