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.

翻译：机器学习模型能够生成从民谣到古典音乐等多种类型的复杂音乐。然而，当前的生成式音乐AI模型通常难以理解并以有意义的方式操控。尽管已有研究开始探索如何为音乐创建可解释人工智能（XAI）生成模型，但在音乐创作实践中尚未有生成式XAI模型被深入研究。本文引入了一项关于MeasureVAE生成式音乐XAI模型的自我民族志研究，该模型具有可解释的潜在维度，并基于爱尔兰民间音乐进行训练。研究结果表明，音乐创作工作流程的探索性特征更突出训练数据集中的音乐特征，而非生成模型本身的特征。在迭代工作流程中对XAI模型的挪用，凸显了XAI模型融入比其初始设计更丰富、更复杂工作流程的潜力。