Particle Filtering on the Audio Localization Manifold
Abstract
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 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.
Cite
@article{arxiv.1003.0659,
title = {Particle Filtering on the Audio Localization Manifold},
author = {Evan Ettinger and Yoav Freund},
journal= {arXiv preprint arXiv:1003.0659},
year = {2010}
}