Online maximum-likelihood learning of time-varying dynamical models in block-frequency-domain

A linear dynamical model can be used to describe the evolution of an unknown system in noisy conditions. However, in most applications model parameters of a dynamical system are not known a priori, bringing into question the optimality of traditional state-only estimators. In this paper, we consider block-frequency-domain dynamical models and formulate an optimal framework for low-latency joint state and parameter estimation. We show that the resulting variational expectation-maximization algorithm in the block-frequency-domain offers a comprehensive and efficient solution for the joint estimation task.