Music generation has emerged as a significant topic in artificial intelligence and machine learning. While recurrent neural networks (RNNs) have been widely employed for sequence generation, generative adversarial networks (GANs) remain relatively underexplored in this domain. This paper presents two systems based on adversarial networks for music generation. The first system learns a set of music pieces without differentiating between styles, while the second system focuses on learning and deviating from specific composers' styles to create innovative music. By extending the Creative Adversarial Networks (CAN) framework to the music domain, this work introduces unrolled CAN to address mode collapse, evaluating both GAN and CAN in terms of creativity and variation.

翻译：音乐生成已成为人工智能和机器学习领域的重要课题。尽管循环神经网络（RNN）已广泛应用于序列生成，但生成对抗网络（GAN）在该领域仍相对缺乏深入探索。本文提出了两种基于对抗网络的音乐生成系统。第一个系统学习一组音乐作品而不区分风格，第二个系统则专注于学习并偏离特定作曲家的风格以创作创新音乐。通过将创意对抗网络（CAN）框架扩展至音乐领域，本研究引入展开式CAN以解决模式崩溃问题，并从创造性和多样性两个维度对GAN和CAN进行了评估。