flowEQ uses a disentangled variational autoencoder (β-VAE) in order to provide a new modality for modifying the timbre of recordings via a parametric equalizer. By traversing the learned latent space of the trained decoder network, the user can more quickly search through the configurations of a five band parametric equalizer. This methodology promotes using one’s ears to determine the proper EQ settings over looking at transfer functions or specific frequency controls. Two main modes of operation are provided (Traverse and Semantic), which allow users to sample from the latent space of the 12 trained models.

Applications:

• Quick and easy timbral adjustments
• Automated timbral shifts over time for creative effects
• Powerful ‘tone controls’ for users in a playback/listening setting

floweq.ml
github.com/csteinmetz1/flowEQ