# Inspecting and Interacting with Meaningful Music Representations using VAE

arXiv: https://arxiv.org/abs/1904.08842 · HTML: https://mlsanity.com/paper/1904.08842/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Applications and Advances of Artificial Intelligence in Music Generation:A Review](https://mlsanity.com/paper/2409.03715/) (2024)
- [Flat Latent Manifolds for Human-machine Co-creation of Music](https://mlsanity.com/paper/2202.12243/) (2022)
- [Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer](https://mlsanity.com/paper/2201.08526/) (2022)
- [Manga Rescreening with Interpretable Screentone Representation](https://mlsanity.com/paper/2306.04114/) (2023)

## Abstract (arXiv)

Variational Autoencoders(VAEs) have already achieved great results on image generation and recently made promising progress on music generation. However, the generation process is still quite difficult to control in the sense that the learned latent representations lack meaningful music semantics. It would be much more useful if people can modify certain music features, such as rhythm and pitch contour, via latent representations to test different composition ideas. In this paper, we propose a new method to inspect the pitch and rhythm interpretations of the latent representations and we name it disentanglement by augmentation. Based on the interpretable representations, an intuitive graphical user interface is designed for users to better direct the music creation process by manipulating the pitch contours and rhythmic complexity.