A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation
This work addresses echo cancellation for audio communication systems, presenting an incremental improvement over existing methods.
The paper tackles the challenge of adapting learning rates in echo cancellation under varying conditions like double-talk and echo path changes by proposing a closed-loop method where the learning rate is proportional to a misalignment parameter estimated via a gradient adaptive approach, demonstrating that it outperforms current double-talk detection techniques by up to 6 dB.
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.