Universal audio synthesizer control with normalizing flows

By Philippe Esling, Naotake Masuda, Adrien Bardet, Romeo Despres, Axel Chemla-Romeu-Santos. 2019

46 citations recorded by the source corpus.

The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a novel formulation of audio synthesizer control. We formalize it as finding an organized latent audio space that represents the capabilities of a synthesizer, while constructing an invertible mapping to the space of its parameters. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce the disentangling flows, which allow to perform the invertible mapping between separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. We also show that our model is able to learn semantic controls of a synthesizer by smoothly mapping to its parameters. Finally, we discuss the use of our model in creative applications and its real-time implementation in Ableton Live

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

In Signal Processing

Citations in the corpus

DDSP: Differentiable Digital Signal Processing (2020, methodology): “Synthesizer outputs modeled with a variational autoencoder were recently used as a “world model” (Ha & Schmidhuber, 2018) to pass approximate gradients to a controller during learning (Esling et al., 2019).”
Normalizing Flows: An Introduction and Review of Current Methods (2020, background): “ation [60], audio generation [27], [53], [77], graph generation [65], reinforcement learning [67], [70], [93], computer graphics [69], and physics [51], [58], [71], [104], [105].”
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed (2022, background): “Applications include image generation [15,17], noise modelling [18], video generation [19], audio generation [20–22], graph generation [23], reinforcement learning [24–26], computer graphics [67], and physics [27–31].”