Abstract
Probabilistic approaches to tracking often use single-source Bayesian models; applying these to multi-source tasks is problematic. We apply a principled multi-object tracking implementation, the Gaussian mixture probability hypothesis density filter, to track multiple sources having fixed pitch plus vibrato. We demonstrate high-quality ltering in a synthetic experiment, and nd improved tracking using a richer feature set which captures underlying dynamics. Our implementation is available as open-source Python code.