“第四十三章 AI谱曲”版本间的差异
来自cslt Wiki
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* RNN Performer [https://magenta.tensorflow.org/performance-rnn] | * RNN Performer [https://magenta.tensorflow.org/performance-rnn] | ||
* Perceiver AR [https://magenta.tensorflow.org/perceiver-ar] | * Perceiver AR [https://magenta.tensorflow.org/perceiver-ar] | ||
| − | * Paint with music [https://magenta.tensorflow.org/paint-with-music | + | * Paint with music [https://magenta.tensorflow.org/paint-with-music] |
* Listen to transformer [https://magenta.github.io/listen-to-transformer/#a1_11806.mid] | * Listen to transformer [https://magenta.github.io/listen-to-transformer/#a1_11806.mid] | ||
* Sketchpad [https://magic-sketchpad.glitch.me/] | * Sketchpad [https://magic-sketchpad.glitch.me/] | ||
* Other Magenta demos [https://magenta.tensorflow.org/demos] | * Other Magenta demos [https://magenta.tensorflow.org/demos] | ||
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==开发者资源== | ==开发者资源== | ||
2023年8月13日 (日) 02:39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]
- Listen to transformer [14]
- Sketchpad [15]
- Other Magenta demos [16]
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. [20]
- Herremans D, Chuan C H, Chew E. A functional taxonomy of music generation systems[J]. ACM Computing Surveys (CSUR), 2017, 50(5): 1-30. [21]
- 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. [22]
- Performance RNN: Generating Music with Expressive Timing and Dynamics [23]
- 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. [24] [25]
- Fernández J D, Vico F. AI methods in algorithmic composition: A comprehensive survey[J]. Journal of Artificial Intelligence Research, 2013, 48: 513-582. [26]
- Hawthorne C, Jaegle A, Cangea C, et al. General-purpose, long-context autoregressive modeling with Perceiver AR[J]. arXiv preprint arXiv:2202.07765, 2022. [27]
