On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk

TL;DR

This paper introduces a new adaptive learning rate method for frequency-domain echo cancellation that improves performance during double-talk conditions by deriving an optimal rate based on noise considerations.

Contribution

It proposes a novel learning rate adjustment technique for frequency-domain echo cancellers, enhancing robustness during double-talk scenarios.

Findings

Outperforms existing double-talk detection methods.

Simple to implement in MDF adaptive filters.

Demonstrates improved echo cancellation performance.

Abstract

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.