《MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and A...
出处：2018 AAAI SourceCode:https://github.com/salu133445/musegan abstract： （写得不错 值得借鉴）重点阐述了生成音乐和生成图片，视频及语音的不同。首先音乐是基于时间序列的；其次音符在和弦、琶音（arpeggios）、旋律、复音等规则的控制之下的；同时一首歌曲是多track的。总之不能简单堆叠音符。本文基于GAN提出了三种模型来生成
相关文章
- 1. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 2. Graphical Generative Adversarial Networks
- 3. Generative Adversarial Networks for Hyperspectral Image Classification
- 4. Binary Generative Adversarial Networks for Image Retrieval
- 5. Triangle Generative Adversarial Networks
- 6. Contrastive Generative Adversarial Networks
- 7. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
- 8. Self-Attention Generative Adversarial Networks
- 9. Generative Adversarial Networks overview（1）
- 10. Generative Adversarial Networks overview（2）
- 更多相关文章...
- • Scala for循环 - Scala教程
- • Lua for 循环 - Lua 教程
- • RxJava操作符（七）Conditional and Boolean
- • Java 8 Stream 教程
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 2. Graphical Generative Adversarial Networks
- 3. Generative Adversarial Networks for Hyperspectral Image Classification
- 4. Binary Generative Adversarial Networks for Image Retrieval
- 5. Triangle Generative Adversarial Networks
- 6. Contrastive Generative Adversarial Networks
- 7. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
- 8. Self-Attention Generative Adversarial Networks
- 9. Generative Adversarial Networks overview（1）
- 10. Generative Adversarial Networks overview（2）