# Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance

arXiv: https://arxiv.org/abs/2006.09833 · HTML: https://mlsanity.com/paper/2006.09833/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model](https://mlsanity.com/paper/2410.02144/) (2024)

## Abstract (arXiv)

We present a controllable neural audio synthesizer based on Gaussian Mixture Variational Autoencoders (GM-VAE), which can generate realistic piano performances in the audio domain that closely follows temporal conditions of two essential style features for piano performances: articulation and dynamics. We demonstrate how the model is able to apply fine-grained style morphing over the course of synthesizing the audio. This is based on conditions which are latent variables that can be sampled from the prior or inferred from other pieces. One of the envisioned use cases is to inspire creative and brand new interpretations for existing pieces of piano music.