  We present a noise-robust adaptation control strategy for block-online
supervised acoustic system identification by exploiting a noise dictionary. The
proposed algorithm takes advantage of the pronounced spectral structure which
characterizes many types of interfering noise signals. We model the noisy
observations by a linear Gaussian Discrete Fourier Transform-domain state space
model whose parameters are estimated by an online generalized
Expectation-Maximization algorithm. Unlike all other state-of-the-art
approaches we suggest to model the covariance matrix of the observation
probability density function by a dictionary model. We propose to learn the
noise dictionary from training data, which can be gathered either offline or
online whenever the system is not excited, while we infer the activations
continuously. The proposed algorithm represents a novel machine-learning based
approach to noise-robust adaptation control which allows for faster convergence
in applications characterized by high-level and non-stationary interfering
noise signals and abrupt system changes.
