# Two Sonification Methods for the MindCube

arXiv: https://arxiv.org/abs/2506.18196 · HTML: https://mlsanity.com/paper/2506.18196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics](https://mlsanity.com/paper/1805.08501/) (2018)
- [Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders](https://mlsanity.com/paper/1904.06215/) (2019)
- [The Accordiatron: A MIDI Controller For Interactive Music](https://mlsanity.com/paper/2010.01574/) (2020)
- [Flat Latent Manifolds for Human-machine Co-creation of Music](https://mlsanity.com/paper/2202.12243/) (2022)

## Abstract (arXiv)

In this work, we explore the musical interface potential of the MindCube, an interactive device designed to study emotions. Embedding diverse sensors and input devices, this interface resembles a fidget cube toy commonly used to help users relieve their stress and anxiety. As such, it is a particularly well-suited controller for musical systems that aim to help with emotion regulation. In this regard, we present two different mappings for the MindCube, with and without AI. With our generative AI mapping, we propose a way to infuse meaning within a latent space and techniques to navigate through it with an external controller. We discuss our results and propose directions for future work.