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。

算法作曲中的自传式探索

An Autoethnographic Exploration of XAI in Algorithmic Composition

Explainable AI has the potential to support more interactive and fluid
co-creative AI systems which can creatively collaborate with people. To do
this, creative AI models need to be amenable to debugging by offering
eXplainable AI (XAI) features which are inspectable, understandable, and
modifiable. However, currently there is very little XAI for the arts. In this
work, we demonstrate how a latent variable model for music generation can be
made more explainable; specifically we extend MeasureVAE which generates
measures of music. We increase the explainability of the model by: i) using
latent space regularisation to force some specific dimensions of the latent
space to map to meaningful musical attributes, ii) providing a user interface
feedback loop to allow people to adjust dimensions of the latent space and
observe the results of these changes in real-time, iii) providing a
visualisation of the musical attributes in the latent space to help people
understand and predict the effect of changes to latent space dimensions. We
suggest that in doing so we bridge the gap between the latent space and the
generated musical outcomes in a meaningful way which makes the model and its
outputs more explainable and more debuggable.

介绍了一种增加可解释性的音乐生成的潜变量模型，通过潜空间正则化、用户界面反馈循环和音乐属性可视化，实现了模型和生成音乐结果的可解释性和可调试性。