论文标题
音乐创造性生成模型的挑战：差异最大化的观点
Challenges in creative generative models for music: a divergence maximization perspective
论文作者
论文摘要
在创造性实践中，生成机器学习（ML）模型的开发是由最新的可用性和预培训模型的可用性提高所促进的，这在艺术家，从业者和表演者中越来越兴趣。然而，在艺术领域中引入此类技术也揭示了多种局限性，这些局限性逃避了科学家使用的当前评估方法。值得注意的是，大多数模型仍然无法生成介于培训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，从新的ML目标的新一般表述开始，该框架是为了描述ML文献中已经存在的可能的含义和解决方案（尤其是对于音频和音乐领域）。我们还讨论了生成模型与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。
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.
