Impossible Rooms

What would a musician choose to play in a room that does not exist? From vast cathedrals to intimate recording studios, musicians readily adapt their performances to the acoustic spaces that surround them. In the absence of real reverberant spaces, musicians often turn to digital simulations---which typically simulate either specific real-world spaces or physically-plausible spaces. However, generative data-driven AI and machine learning systems can be used to create not only in-distribution examples that resemble the real world, but also out-of-distribution examples that depart from physical constraints. In this work, we train a fixed-topology feedback delay network (FDN) model on a corpus of real-world room impulse responses, and explore the periphery of the resulting latent space to generate a variety of physically implausible or impossible acoustic spaces. We use the system in a live musical performance where we create an improvised composition in real-time while immersed in a simulated ``impossible acoustic space.'' We present this work as a case study for how AI systems can expand artistic practice by altering the conditions of creation: introducing unfamiliar contexts that invite new forms of musical response while preserving artistic agency.