Year: 2025
Type: preprint
Abstract: Generative artificial intelligence raises concerns related to energy consumption, copyright infringement and creative atrophy. We show that randomly initialized recurrent neural networks can produce arpeggios and low-frequency oscillations that are rich and configurable. In contrast to end-to-end music generation that aims to replace musicians, our approach expands their creativity while requiring no data and much less computational power. More information can be found at: https://allendia.com/
Source: arXiv (Cornell University)
Authors: Chateau-Laurent, Hugo, Vanhatalo, Tara, Pan, Wei-Tung, Hinaut, Xavier
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910513992
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