  The attenuation of acoustic loudspeaker echoes remains to be one of the open
challenges to achieve pleasant full-duplex hands free speech communication. In
many modern signal enhancement interfaces, this problem is addressed by a
linear acoustic echo canceler which subtracts a loudspeaker echo estimate from
the recorded microphone signal. To obtain precise echo estimates, the
parameters of the echo canceler, i.e., the filter coefficients, need to be
estimated quickly and precisely from the observed loudspeaker and microphone
signals. For this a sophisticated adaptation control is required to deal with
high-power double-talk and rapidly track time-varying acoustic environments
which are often faced with portable devices. In this paper, we address this
problem by end-to-end deep learning. In particular, we suggest to infer the
step-size for a least mean squares frequency-domain adaptive filter update by a
Deep Neural Network (DNN). Two different step-size inference approaches are
investigated. On the one hand broadband approaches, which use a single DNN to
jointly infer step-sizes for all frequency bands, and on the other hand
narrowband methods, which exploit individual DNNs per frequency band. The
discussion of benefits and disadvantages of both approaches leads to a novel
hybrid approach which shows improved echo cancellation while requiring only
small DNN architectures. Furthermore, we investigate the effect of different
loss functions, signal feature vectors, and DNN output layer architectures on
the echo cancellation performance from which we obtain valuable insights into
the general design and functionality of DNN-based adaptation control
algorithms.
