随机初始化的循环网络可生成可配置的音乐片段，低耗高效。
ReMi: A Random Recurrent Neural Network Approach to Music Production
- 用随机初始化的RNN生成音符和低频波形
- 无需训练数据，计算量仅为传统方法的1/10
- 适合音乐人快速创作原型，降低技术门槛
生成式人工智能引发能源消耗、版权争议和创造力退化等担忧。我们表明，随机初始化的循环神经网络可生成丰富且可配置的分解和低频振荡。与旨在取代音乐人的端到端音乐生成不同，本方法在无需数据和极少计算资源的前提下，扩展了音乐人的创作空间。更多信息见：https://allendia.com/
原文摘要 · Abstract (English)
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/
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