第四十三章 AI谱曲 - 版本历史

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‎高级读者

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‎开发者资源

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‎演示链接

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‎演示链接

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Cslt：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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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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==教学资料==

* [[教学参考-43|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？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485966&idx=1&sn=5c09008e76340bc192e5c569a05f9cab&chksm=c30800ccf47f89da2048a454658b7ffec5246a043cd71051f064377221b2c264a7d625989113&scene=178#rd]

==扩展阅读==

* 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]

==视频展示==

==演示链接==

* DeepJ [https://github.com/calclavia/DeepJ/tree/icsc/archives/v1]
* RNN Performer [https://magenta.tensorflow.org/performance-rnn]

==开发者资源==

* Performance RNN [https://github.com/magenta/magenta/tree/main/magenta/models/performance_rnn]

==高级读者==

* S. A. Hedges. 1978. Dice music in the eighteenth century. Music Lett. 59, 2 (1978), 180--187. [https://www.jstor.org/stable/734136#metadata_info_tab_contents]
* Herremans D, Chuan C H, Chew E. A functional taxonomy of music generation systems[J]. ACM Computing Surveys (CSUR), 2017, 50(5): 1-30. [https://arxiv.org/pdf/1812.04186]
* 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]
* 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/]

@@ 第34行： / 第34行： @@
 ==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]
-−
 ==高级读者==

@@ 第27行： / 第27行： @@
 * Perceiver AR [https://magenta.tensorflow.org/perceiver-ar]
 * Paint with music [https://magenta.tensorflow.org/paint-with-music]
 * Listen to transformer [https://magenta.github.io/listen-to-transformer/#a1_11806.mid]
 * Sketchpad [https://magic-sketchpad.glitch.me/]

@@ 第26行： / 第26行： @@
 * 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 *]
+* Paint with music [https://magenta.tensorflow.org/paint-with-music]
 * 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]
 ==开发者资源==

@@ 第26行： / 第26行： @@
 * 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]
+* 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]
 * Sketchpad [https://magic-sketchpad.glitch.me/]

@@ 第10行： / 第10行： @@
 * 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]
 * DeepMind Perceiver AR [https://www.deepmind.com/publications/perceiver-ar-general-purpose-long-context-autoregressive-generation]
 * Magenta [https://magenta.tensorflow.org/]

@@ 第44行： / 第44行： @@
 * 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]
-* 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/]
 * 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]