Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911911582367744 |
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| author | Kilb, Justin Ellis, Caroline |
| author_facet | Kilb, Justin Ellis, Caroline |
| contents | This study explores the application of evolutionary generative algorithms in music production to preserve and enhance human creativity. By integrating human feedback into Differential Evolution algorithms, we produced six songs that were submitted to international record labels, all of which received contract offers. In addition to testing the commercial viability of these methods, this paper examines the long-term implications of content generation using traditional machine learning methods compared with evolutionary algorithms. Specifically, as current generative techniques continue to scale, the potential for computer-generated content to outpace human creation becomes likely. This trend poses a risk of exhausting the pool of human-created training data, potentially forcing generative machine learning models to increasingly depend on their random input functions for generating novel content. In contrast to a future of content generation guided by aimless random functions, our approach allows for individualized creative exploration, ensuring that computer-assisted content generation methods are human-centric and culturally relevant through time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05873 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation Kilb, Justin Ellis, Caroline Neural and Evolutionary Computing Artificial Intelligence Optimization and Control This study explores the application of evolutionary generative algorithms in music production to preserve and enhance human creativity. By integrating human feedback into Differential Evolution algorithms, we produced six songs that were submitted to international record labels, all of which received contract offers. In addition to testing the commercial viability of these methods, this paper examines the long-term implications of content generation using traditional machine learning methods compared with evolutionary algorithms. Specifically, as current generative techniques continue to scale, the potential for computer-generated content to outpace human creation becomes likely. This trend poses a risk of exhausting the pool of human-created training data, potentially forcing generative machine learning models to increasingly depend on their random input functions for generating novel content. In contrast to a future of content generation guided by aimless random functions, our approach allows for individualized creative exploration, ensuring that computer-assisted content generation methods are human-centric and culturally relevant through time. |
| title | Conserving Human Creativity with Evolutionary Generative Algorithms: A Case Study in Music Generation |
| topic | Neural and Evolutionary Computing Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2406.05873 |