音乐创造性生成模型的挑战：发散最大化视角
Challenges in creative generative models for music: a divergence maximization perspective
摘要：
通过最近预训练模型的可用性和可用性的改进，创造性实践中生成机器学习（ML）模型的发展正在引起艺术家，从业者和表演者越来越多的兴趣。然而，在艺术领域引入这种技术也暴露出许多局限性，这些局限性逃脱了科学家目前使用的评估方法。值得注意的是，大多数模型仍然无法生成位于训练数据集定义的域之外的内容。在本文中，我们提出了一个替代的前瞻性框架，从一个新的一般制定的ML目标，我们得出描绘可能的影响和解决方案，已经存在于ML文献（特别是音频和音乐领域）。我们还讨论了生成模型和计算创造力之间的现有关系，以及我们的框架如何帮助解决现有模型中缺乏创造力的问题。
英文摘要：
The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising more and more interest among artists, practitioners and performers. Yet, the introduction of such techniques in artistic domains also revealed multiple limitations that escape current evaluation methods used by scientists. Notably, most models are still unable to generate content that lay outside of the domain defined by the training dataset. In this paper, we propose an alternative prospective framework, starting from a new general formulation of ML objectives, that we derive to delineate possible implications and solutions that already exist in the ML literature (notably for the audio and musical domain). We also discuss existing relations between generative models and computational creativity and how our framework could help address the lack of creativity in existing models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
