A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation

TL;DR

This paper introduces a robust frequency domain echo canceller with a novel closed-loop learning rate adaptation that improves performance during double-talk and echo path changes.

Contribution

A new closed-loop learning rate adaptation method based on misalignment estimation for MDF echo cancellers is proposed, enhancing robustness and performance.

Findings

Outperforms existing double-talk detection techniques by up to 6 dB

Effective in varying echo path conditions

Improves convergence stability during double-talk

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. 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.