论文标题
使用深度学习的双轨音乐发电
Dual-track Music Generation using Deep Learning
论文作者
论文摘要
从某种意义上说，音乐发电总是很有趣的，没有正式的食谱。在这项工作中，我们提出了一种新颖的双轨体系结构，用于生成古典钢琴音乐，该钢琴音乐能够建模左手和右手钢琴音乐的相互依赖性。特别是，我们尝试了许多不同模型的神经网络以及音乐的不同表示，结果表明，我们提出的模型的表现优于所有其他测试方法。此外，我们为模型培训和生成部署了一些特殊的政策，这极大地促进了模型性能。最后，在两种评估方法下，我们将模型与Musegan项目和真实音乐进行了比较。
Music generation is always interesting in a sense that there is no formalized recipe. In this work, we propose a novel dual-track architecture for generating classical piano music, which is able to model the inter-dependency of left-hand and right-hand piano music. Particularly, we experimented with a lot of different models of neural network as well as different representations of music, and the results show that our proposed model outperforms all other tested methods. Besides, we deployed some special policies for model training and generation, which contributed to the model performance remarkably. Finally, under two evaluation methods, we compared our models with the MuseGAN project and true music.
