  One of the main difficulties in echo cancellation is the fact that the
learning rate needs to vary according to conditions such as double-talk and
echo path change. Several methods have been proposed to vary the learning. In
this paper we propose a new closed-loop method where the learning rate is
proportional to a misalignment parameter, which is in turn estimated based on a
gradient adaptive approach. The method is presented in the context of a
multidelay block frequency domain (MDF) echo canceller. We demonstrate that the
proposed algorithm outperforms current popular double-talk detection techniques
by up to 6 dB.
