“第四十三章 AI谱曲”版本间的差异
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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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* 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][https://artsandculture.google.com/experiment/paint-with-music/YAGuJyDB-XbbWg] | + | * Paint with music [https://magenta.tensorflow.org/paint-with-music] |
| + | * 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] | * Listen to transformer [https://magenta.github.io/listen-to-transformer/#a1_11806.mid] | ||
| + | * 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== | ||
| − | * DeepJ [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1] | + | * 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] | + | * Perciver AR [*][https://github.com/google-research/perceiver-ar] |
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==高级读者== | ==高级读者== | ||
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* 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. [https://www.aes.org/e-lib/browse.cfm?elib=189] | * 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. [https://www.aes.org/e-lib/browse.cfm?elib=189] | ||
* Performance RNN: Generating Music with Expressive Timing and Dynamics [https://magenta.tensorflow.org/performance-rnn] | * Performance RNN: Generating Music with Expressive Timing and Dynamics [https://magenta.tensorflow.org/performance-rnn] | ||
| − | * 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] | * 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] | ||
2023年8月13日 (日) 02:41的最后版本
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]
- 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]
