In this paper we present a system that is capable of tracking the pitch and volume of a musical source by making use of training data. We show how we can use pitch-tagged training example sounds to construct a model of a target source, and then use that model to track such a source in unseen mixtures. We do so using a regularized decomposition approach that is designed to strive for semantic continuity in its estimates.