“第四十三章 AI谱曲”版本间的差异
来自cslt Wiki
（以“==教学资料== * 教学参考 * [http://aigraph.cslt.org/courses/43/course-43.pptx 课件] * 小清爱提问：计算机如何谱曲？ [http...”为内容创建页面） |
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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] | ||
| + | * DeepMind Perceiver AR [https://www.deepmind.com/publications/perceiver-ar-general-purpose-long-context-autoregressive-generation] | ||
| + | * Magenta [https://magenta.tensorflow.org/] | ||
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==视频展示== | ==视频展示== | ||
| + | * Perceiver AR [http://aigraph.cslt.org/courses/43/autoregressive-long-context-music-generation.mp4] | ||
| + | * DDSP-VST, 将任何声音转换成音乐，模拟变化的基音和响度 [http://aigraph.cslt.org/courses/43/multi-instrument-transitions.mp4] | ||
==演示链接== | ==演示链接== | ||
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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] | ||
| + | * Paint with music [https://magenta.tensorflow.org/paint-with-music][https://artsandculture.google.com/experiment/paint-with-music/YAGuJyDB-XbbWg] | ||
| + | * Listen to transformer [https://magenta.github.io/listen-to-transformer/#a1_11806.mid] | ||
| + | * Other Magenta demos [https://magenta.tensorflow.org/demos] | ||
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==开发者资源== | ==开发者资源== | ||
| + | * DeepJ [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1] | ||
* Performance RNN [https://github.com/magenta/magenta/tree/main/magenta/models/performance_rnn] | * 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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* 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. [https://arxiv.org/pdf/1801.00887.pdf] [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1] | * 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. [https://arxiv.org/pdf/1801.00887.pdf] [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1] | ||
* Fernández J D, Vico F. AI methods in algorithmic composition: A comprehensive survey[J]. Journal of Artificial Intelligence Research, 2013, 48: 513-582. [https://www.jair.org/index.php/jair/article/download/10845/25883/] | * Fernández J D, Vico F. AI methods in algorithmic composition: A comprehensive survey[J]. Journal of Artificial Intelligence Research, 2013, 48: 513-582. [https://www.jair.org/index.php/jair/article/download/10845/25883/] | ||
| + | * Hawthorne C, Jaegle A, Cangea C, et al. General-purpose, long-context autoregressive modeling with Perceiver AR[J]. arXiv preprint arXiv:2202.07765, 2022. [https://arxiv.org/pdf/2202.07765] | ||
2022年8月22日 (一) 13:28的版本
教学资料
扩展阅读
视频展示
演示链接
- RNN Performer [9]
- Perceiver AR [10]
- Paint with music [11][12]
- Listen to transformer [13]
- Other Magenta demos [14]
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. [18]
- Herremans D, Chuan C H, Chew E. A functional taxonomy of music generation systems[J]. ACM Computing Surveys (CSUR), 2017, 50(5): 1-30. [19]
- 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. [20]
- Performance RNN: Generating Music with Expressive Timing and Dynamics [21]
- 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. [22] [23]
- Fernández J D, Vico F. AI methods in algorithmic composition: A comprehensive survey[J]. Journal of Artificial Intelligence Research, 2013, 48: 513-582. [24]
- Hawthorne C, Jaegle A, Cangea C, et al. General-purpose, long-context autoregressive modeling with Perceiver AR[J]. arXiv preprint arXiv:2202.07765, 2022. [25]
