On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk
This work addresses echo cancellation in telecommunications, which is an incremental improvement for handling double-talk scenarios.
The paper tackles the challenge of adjusting the learning rate in frequency-domain echo cancellation under conditions like double-talk and echo path changes by proposing a new method based on deriving the optimal learning rate for the NLMS algorithm in noisy environments, showing it outperforms current double-talk detection techniques and is simple to implement.
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. In this paper we propose a new method of varying the learning rate of a frequency-domain echo canceller. This method is based on the derivation of the optimal learning rate of the NLMS algorithm in the presence of noise. The method is evaluated in conjunction with the multidelay block frequency domain (MDF) adaptive filter. We demonstrate that it performs better than current double-talk detection techniques and is simple to implement.