Machine Learning models are capable of generating complex music across a
range of genres from folk to classical music. However, current generative music
AI models are typically difficult to understand and control in meaningful ways.
Whilst research has started to explore how explainable AI (XAI) generative
models might be created for music, no generative XAI models have been studied
in music making practice. This paper introduces an autoethnographic study of
the use of the MeasureVAE generative music XAI model with interpretable latent
dimensions trained on Irish folk music. Findings suggest that the exploratory
nature of the music-making workflow foregrounds musical features of the
training dataset rather than features of the generative model itself. The
appropriation of an XAI model within an iterative workflow highlights the
potential of XAI models to form part of a richer and more complex workflow than
they were initially designed for.

该研究使用 MeasureVAE 可解释的潜在维度的生成音乐 XAI 模型在爱尔兰民间音乐的训练数据集上进行了自传体研究，结果显示探索性的音乐创作流程突显了训练数据集的音乐特征而非生成模型本身的特征，XAI 模型在迭代工作流中的应用显示出其成为比其最初设计用途更丰富和复杂工作流的潜力。