  We present a novel end-to-end deep learning-based adaptation control
algorithm for frequency-domain adaptive system identification. The proposed
method exploits a deep neural network to map observed signal features to
corresponding step-sizes which control the filter adaptation. The parameters of
the network are optimized in an end-to-end fashion by minimizing the average
normalized system distance of the adaptive filter. This avoids the need of
explicit signal power spectral density estimation as required for model-based
adaptation control and further auxiliary mechanisms to deal with model
inaccuracies. The proposed algorithm achieves fast convergence and robust
steady-state performance for scenarios characterized by high-level, non-white
and non-stationary additive noise signals, abrupt environment changes and
additional model inaccuracies.
