  We present a method for simultaneously localizing multiple sound sources
within a visual scene. This task requires a model to both group a sound mixture
into individual sources, and to associate them with a visual signal. Our method
jointly solves both tasks at once, using a formulation inspired by the
contrastive random walk of Jabri et al. We create a graph in which images and
separated sounds correspond to nodes, and train a random walker to transition
between nodes from different modalities with high return probability. The
transition probabilities for this walk are determined by an audio-visual
similarity metric that is learned by our model. We show through experiments
with musical instruments and human speech that our model can successfully
localize multiple sounds, outperforming other self-supervised methods. Project
site: https://hxixixh.github.io/mix-and-localize
