Steering latent audio models through interactive machine learning

In this paper, we present a proof-of-concept mechanism for steering latent audio models through interactive machine learning. Our approach involves mapping the human-performance space to the high-dimensional, computer-generated latent space of a neural audio model by utilizing a regressive model learned from a set of demonstrative actions. By implementing this method in ideation, exploration, and sound and music performance we have observed its efficiency, flexibility, and immediacy of control over generative audio processes.