# Exploring XAI for the Arts: Explaining Latent Space in Generative Music

arXiv: https://arxiv.org/abs/2308.05496 · HTML: https://mlsanity.com/paper/2308.05496/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 62 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [Fader Networks: Manipulating Images by Sliding Attributes](https://mlsanity.com/paper/1706.00409/) (2017)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [Deep Learning Techniques for Music Generation -- A Survey](https://mlsanity.com/paper/1709.01620/) (2017)
- [Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling](https://mlsanity.com/paper/2007.15474/) (2020)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [A Functional Taxonomy of Music Generation Systems](https://mlsanity.com/paper/1812.04186/) (2018)
- [Deep Music Analogy Via Latent Representation Disentanglement](https://mlsanity.com/paper/1906.03626/) (2019)
- [PIANOTREE VAE: Structured Representation Learning for Polyphonic Music](https://mlsanity.com/paper/2008.07118/) (2020)
- [Learning to Traverse Latent Spaces for Musical Score Inpainting](https://mlsanity.com/paper/1907.01164/) (2019)
- [GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures](https://mlsanity.com/paper/1707.04588/) (2017)
- [To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles](https://mlsanity.com/paper/2006.11684/) (2020)
- [ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2008.05731/) (2020)
- [Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload](https://mlsanity.com/paper/1905.08878/) (2019)
- [In a Silent Way: Communication Between AI and Improvising Musicians Beyond Sound](https://mlsanity.com/paper/1902.06442/) (2019)
- [Explainable Computational Creativity](https://mlsanity.com/paper/2205.05682/) (2022)
- [Hallmarks of Human-Machine Collaboration: A framework for assessment in the DARPA Communicating with Computers Program](https://mlsanity.com/paper/2102.04958/) (2021)
- [Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail](https://mlsanity.com/paper/2008.07376/) (2020)
- [A Laptop Ensemble Performance System using Recurrent Neural Networks](https://mlsanity.com/paper/2012.02322/) (2020)
- [Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders](https://mlsanity.com/paper/2106.08867/) (2021)

## Built on by (most cited first, 8 of 8)

- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [An Autoethnographic Exploration of XAI in Algorithmic Composition](https://mlsanity.com/paper/2308.06089/) (2023)
- [Exploring Latent Spaces of Tonal Music using Variational Autoencoders](https://mlsanity.com/paper/2311.03621/) (2023)
- [AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe](https://mlsanity.com/paper/2604.20936/) (2026)
- [JAMMIN-GPT: Text-based Improvisation using LLMs in Ableton Live](https://mlsanity.com/paper/2312.03479/) (2023)
- [Encoding of musical structures in hidden units of restricted Boltzmann machines](https://mlsanity.com/paper/2509.04899/) (2025)
- [Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability](https://mlsanity.com/paper/2607.22428/) (2026)
- [MI-MIDI: Mechanistic Interpretability of Text-to-MIDI Generation Models via Probing, Lenses and Steering](https://mlsanity.com/paper/2608.06638/) (2026)

## Abstract (arXiv)

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.