用可展开的对抗网络生成新音乐，避免风格单一
Unrolled Creative Adversarial Network For Generating Novel Musical Pieces
- 提出可展开的创意对抗网络，缓解生成模式崩溃
- 在无风格区分和特定作曲家风格上均实现创新生成
- 适合对音乐生成与对抗学习感兴趣的开发者
音乐生成已成为人工智能与机器学习的重要课题。尽管循环神经网络（RNN）广泛用于序列生成，生成式对抗网络（GAN）在该领域仍相对未被充分探索。本文提出两种基于对抗网络的音乐生成系统：第一个系统不区分风格地学习音乐片段；第二个系统聚焦于学习并偏离特定作曲家的风格以生成创新作品。通过将创意对抗网络（CAN）框架拓展至音乐领域，并引入可展开的CAN以解决模式崩溃问题，本工作从创造力与多样性角度评估了GAN与CAN的表现。
原文摘要 · Abstract (English)
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.
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