“第四十三章 AI谱曲”版本间的差异

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==开发者资源==
 
==开发者资源==
  
* DeepJ [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1]
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* DeepJ [*][https://github.com/calclavia/DeepJ/tree/icsc/archives/v1]
* Performance RNN [https://github.com/magenta/magenta/tree/main/magenta/models/performance_rnn]
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* Performance RNN [*][https://github.com/magenta/magenta/tree/main/magenta/models/performance_rnn]
* Perciver AR [https://github.com/google-research/perceiver-ar]
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* Perciver AR [*][https://github.com/google-research/perceiver-ar]
 
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==高级读者==
 
==高级读者==

2023年8月13日 (日) 02:40的版本

教学资料


扩展阅读

  • AI100问:计算机如何谱曲?[2]
  • 维基百科:计算机谱曲 [3][4]
  • 维基百科:ILLIAC suite [5]
  • 维基百科:Lejaren Hiller [6]
  • DeepMind Perceiver AR [7]
  • Magenta [8]


视频展示

  • Perceiver AR [9]
  • DDSP-VST, 将任何声音转换成音乐,模拟变化的基音和响度 [10]

演示链接

  • RNN Performer [11]
  • Perceiver AR [12]
  • Paint with music [13]
  • Paint with music [*][14]
  • Listen to transformer [15]
  • Sketchpad [16]
  • Other Magenta demos [17]

开发者资源

高级读者

  • S. A. Hedges. 1978. Dice music in the eighteenth century. Music Lett. 59, 2 (1978), 180--187. [21]
  • Herremans D, Chuan C H, Chew E. A functional taxonomy of music generation systems[J]. ACM Computing Surveys (CSUR), 2017, 50(5): 1-30. [22]
  • A. Hiller Jr, L. and L. M. Isaacson. 1957. Musical composition with a high speed digital computer. In Audio Engineering Society Convention 9. Audio Engineering Society. [23]
  • Performance RNN: Generating Music with Expressive Timing and Dynamics [24]
  • Mao H H, Shin T, Cottrell G. DeepJ: Style-specific music generation[C]//2018 IEEE 12th International Conference on Semantic Computing (ICSC). IEEE, 2018: 377-382. [25] [26]
  • Fernández J D, Vico F. AI methods in algorithmic composition: A comprehensive survey[J]. Journal of Artificial Intelligence Research, 2013, 48: 513-582. [27]
  • Hawthorne C, Jaegle A, Cangea C, et al. General-purpose, long-context autoregressive modeling with Perceiver AR[J]. arXiv preprint arXiv:2202.07765, 2022. [28]