论文标题
Gans＆Reels：使用生成对抗网络创建爱尔兰音乐
GANs & Reels: Creating Irish Music using a Generative Adversarial Network
论文作者
论文摘要
在本文中，我们提出了一种使用无复发组件的生成对抗网络来生成算法旋律的方法。音乐发电已经成功地使用了经常性的神经网络完成，该模型在其中学习可以帮助创建真实的旋律的序列信息。在这里，我们使用带有扩张的卷积和塔的DC-GAN架构来捕获连续信息作为空间图像信息，并以固定长度的旋律形式（例如爱尔兰传统卷轴）学习长期依赖性。
In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components. Music generation has been successfully done using recurrent neural networks, where the model learns sequence information that can help create authentic sounding melodies. Here, we use DC-GAN architecture with dilated convolutions and towers to capture sequential information as spatial image information, and learn long-range dependencies in fixed-length melody forms such as Irish traditional reel.
