  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.
