Tracking noise via dynamical systems with a continuum of states

We model noise as a sequence of states of a dynamical system with a continuum of states. Observations generated by such a system are assumed to be related to the state of the system by a functional relation which models clean speech as the corrupting influence on noise. We show how the closed-form representation of such a dynamical system can be rendered tractable and solved iteratively by dynamically sampling the state space, resulting in an estimated noise sequence (sequence of states), which can then be removed from the noisy speech signal by standard methods. Experiments on speech corrupted by various noises show that the proposed algorithm performs better than our best previous algorithm, VTS, which assumes that the noise is stationary.