“第四十三章 AI谱曲”版本间的差异
来自cslt Wiki
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* AI100问:计算机如何谱曲?[http://aigraph.cslt.org/ai100/AI-100-76-计算机如何谱曲.pdf] | * AI100问:计算机如何谱曲?[http://aigraph.cslt.org/ai100/AI-100-76-计算机如何谱曲.pdf] | ||
* 维基百科:计算机谱曲 [http://aigraph.cslt.org/courses/43/算法作曲.pdf][http://aigraph.cslt.org/courses/43/Algorithmic_composition.pdf] | * 维基百科:计算机谱曲 [http://aigraph.cslt.org/courses/43/算法作曲.pdf][http://aigraph.cslt.org/courses/43/Algorithmic_composition.pdf] | ||
+ | * 维基百科:ILLIAC suite [http://aigraph.cslt.org/courses/43/Illiac_Suite.pdf] | ||
+ | * 维基百科:Lejaren Hiller [http://aigraph.cslt.org/courses/43/Lejaren_Hiller.pdf] | ||
* DeepMind Perceiver AR [https://www.deepmind.com/publications/perceiver-ar-general-purpose-long-context-autoregressive-generation] | * DeepMind Perceiver AR [https://www.deepmind.com/publications/perceiver-ar-general-purpose-long-context-autoregressive-generation] | ||
* Magenta [https://magenta.tensorflow.org/] | * Magenta [https://magenta.tensorflow.org/] | ||
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2022年8月22日 (一) 13:54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
演示链接
- RNN Performer [11]
- Perceiver AR [12]
- Paint with music [13][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]