Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction

TL;DR

This paper introduces a joint approach for acoustic echo cancellation and blind source extraction that improves multi-microphone audio processing by maximizing source independence and efficiently estimating filters.

Contribution

It presents a novel joint algorithm that simultaneously estimates AEC and beamforming filters using a maximum-likelihood approach, enhancing efficiency and robustness.

Findings

Joint AEC and BSE outperform individual filter updates.

The method is robust to double talk scenarios.

Evaluation shows improved echo suppression and source extraction.

Abstract

We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AEC and beamforming filters by maximizing the statistical independence of a non-Gaussian source of interest and a stationary Gaussian background modeling interfering signals and residual echo. Double talk-robust and fast-converging parameter updates are derived from a global maximum-likelihood objective function resulting in a computationally efficient Newton-type update rule. Evaluation with simulated acoustic data confirms the benefit of the proposed joint AEC and beamforming filter estimation in comparison to updating both filters individually.