# Audio-guided Album Cover Art Generation with Genetic Algorithms

**Authors:** James Marien, Sam Leroux, Bart Dhoedt, Cedric De Boom  
**arXiv:** [2207.07162](https://arxiv.org/abs/2207.07162)  
**Date:** 2022-07-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2207.07162v1/

## Key points

- Generating album cover art guided by audio features is a challenge in the music industry.
- Designing cover art can be expensive and daunting for non-professional artists.
- The authors propose a deep-learning framework that uses genetic algorithms to generate cover art based on audio features.
- The framework offers flexibility and individual components can be easily replaced without retraining the entire system.
- Genetic algorithms are used to overcome optimization challenges such as bad local minima and adversarial examples.
- The framework is capable of generating suitable cover art for most genres, with visual features adapting to changes in audio features.
- The research highlights the importance of captivating cover art in capturing listeners' attention amidst intense competition in the music industry.
- The proposed framework makes designing cover art accessible even to non-professional artists.
- Further advancements and applications in audio-guided visual generation tasks are possible based on this research.

## Summary

This paper addresses the challenge of generating album cover art that is guided by audio features. With over 60,000 songs released on Spotify every day, it is crucial for artists to have captivating and inviting cover art that reflects their music's character and their own identity. However, designing cover art can be a daunting and expensive process, especially for non-professional artists. To overcome this challenge, the authors propose a novel deep-learning framework that utilizes genetic algorithms to generate cover art based on audio features. Inspired by VQGAN-CLIP, their approach offers flexibility as individual components can be easily replaced without the need for retraining the entire framework. The paper outlines the architectural details of their models and discusses the optimization challenges they encountered. They utilize genetic algorithms to overcome issues such as bad local minima and adversarial examples. The authors find that their framework is capable of generating suitable cover art for most genres, with visual features adapting to changes in audio features. Based on these results, the authors believe that their framework opens up possibilities for extensions and more advanced applications in audio-guided visual generation tasks. In conclusion, this paper presents a flexible framework for generating album cover art guided by audio features. It highlights the importance of captivating cover art in capturing listeners' attention amidst intense competition in the music industry. The proposed framework offers a solution to the creative and costly process of designing cover art, making it accessible even to non-professional artists. By utilizing genetic algorithms and addressing optimization challenges, the framework demonstrates its ability to generate suitable cover art across various genres. Overall, this research paves the way for further advancements and applications in audio-guided visual generation tasks.