论文标题
Gan可以发起新的电子舞蹈音乐流派吗？ - 使用gan产生新颖的节奏模式，并流派歧义丧失
Can GAN originate new electronic dance music genres? -- Generating novel rhythm patterns using GAN with Genre Ambiguity Loss
论文作者
论文摘要
自从引入深度学习以来，研究人员提出了使用深度学习的内容生成系统，并证明他们有能力产生令人信服的内容和艺术成果，包括音乐。但是，人们可以说，这些基于深度学习的系统模仿并重现了人类创造的内容内固有的模式，而不是产生新的和创造性的东西。本文着重于音乐发电，尤其是电子舞蹈音乐的节奏模式，并讨论我们是否可以使用深度学习来产生新颖的节奏，这是训练数据集中找不到的有趣模式。我们扩展了生成对抗网络（GAN）的框架，并鼓励它通过在框架中添加其他分类器来与数据集固有的分布不同。该论文表明，我们提出的gan可以产生听起来像音乐节奏的节奏模式，但不属于训练数据集中的任何流派。我们的网站可在我们的网站上找到源代码，生成的节奏模式和用于流行数字音频工作站软件的补充插件软件。
Since the introduction of deep learning, researchers have proposed content generation systems using deep learning and proved that they are competent to generate convincing content and artistic output, including music. However, one can argue that these deep learning-based systems imitate and reproduce the patterns inherent within what humans have created, instead of generating something new and creative. This paper focuses on music generation, especially rhythm patterns of electronic dance music, and discusses if we can use deep learning to generate novel rhythms, interesting patterns not found in the training dataset. We extend the framework of Generative Adversarial Networks(GAN) and encourage it to diverge from the dataset's inherent distributions by adding additional classifiers to the framework. The paper shows that our proposed GAN can generate rhythm patterns that sound like music rhythms but do not belong to any genres in the training dataset. The source code, generated rhythm patterns, and a supplementary plugin software for a popular Digital Audio Workstation software are available on our website.
