Flow Synthetizer

Sound synthesizers are pervasive in music and they now even entirely define new music genres. However, their complexity and sets of parameters renders them difficult to master. We created an innovative generative probabilistic model that learns an invertible mapping between a continuous auditory latent space of a synthesizer audio capabilities and the space of its parameters. We approach this task using variational auto-encoders and normalizing flows using this new learning model, we can learn the principal macro-controls of a synthesizer, allowing to travel across its organized manifold of sounds, performing parameter inference from audio to control the synthesizer with our voice, and even address semantic dimension learning where we find how the controls fit to given semantic concepts, all within a single model.