We present and discuss the Agent Designer, a system that enables users of digital audio workstations to generate novel high-level structures for their compositions based on previous examples. The system uses variable-order Markov models and rule induction to learn both temporal relations and structural relations between parts in a piece of music. As is usual in machine learning, however, the quality of the learning can be improved greatly by users specifying relevant features. The Agent Designer therefore points to important design and human–computer interaction problems, as well as algorithmic challenges. We present a number of studies that help to understand how effective the Agent Designer is and how we might design a user interface that best enables users to obtain quality results from the system. We show that the Agent Designer is effective for certain musical styles, such as loop-based electronic music, and that we as expert users can design agents that produce the most effective results. We also note that it remains a challenge to automate this process fully.

中文翻译：

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实时排序和并列短音乐模式的代理的设计和评估

我们介绍并讨论 Agent Designer，该系统使数字音频工作站的用户能够根据之前的示例为其作品生成新颖的高级结构。该系统使用变阶马尔可夫模型和规则归纳来学习一段音乐中各部分之间的时间关系和结构关系。然而，与机器学习中的常见情况一样，用户指定相关特征可以大大提高学习质量。因此，Agent Designer 指出了重要的设计和人机交互问题，以及算法挑战。我们提供了许多研究，这些研究有助于了解 Agent Designer 的有效性以及我们如何设计一个用户界面，以最好地使用户能够从系统中获得高质量的结果。我们表明代理设计器对某些音乐风格有效，例如基于循环的电子音乐，并且我们作为专家用户可以设计产生最有效结果的代理。我们还注意到，完全自动化这个过程仍然是一个挑战。