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We present a noise-robust adaptation control strategy for block-onlinesupervised acoustic system identification by exploiting a noise dictionary. Theproposed algorithm takes advantage of the pronounced spectral structure whichcharacterizes many types of interfering noise signals. We model the noisyobservations by a linear Gaussian Discrete Fourier Transform-domain state spacemodel whose parameters are estimated by an online generalizedExpectation-Maximization algorithm. Unlike all other state-of-the-artapproaches we suggest to model the covariance matrix of the observationprobability density function by a dictionary model. We propose to learn thenoise dictionary from training data, which can be gathered either offline oronline whenever the system is not excited, while we infer the activationscontinuously. The proposed algorithm represents a novel machine-learning basedapproach to noise-robust adaptation control which allows for faster convergencein applications characterized by high-level and non-stationary interferingnoise signals and abrupt system changes.

Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting A Noise Dictionary