  We present a novel particle filtering algorithm for tracking a moving sound
source using a microphone array. If there are N microphones in the array, we
track all $N \choose 2$ delays with a single particle filter over time. Since
it is known that tracking in high dimensions is rife with difficulties, we
instead integrate into our particle filter a model of the low dimensional
manifold that these delays lie on. Our manifold model is based off of work on
modeling low dimensional manifolds via random projection trees [1]. In
addition, we also introduce a new weighting scheme to our particle filtering
algorithm based on recent advancements in online learning. We show that our
novel TDOA tracking algorithm that integrates a manifold model can greatly
outperform standard particle filters on this audio tracking task.
