A Comparative Analysis of Generalised Echo and Interference Cancelling and Extended Multichannel Wiener Filtering for Combined Noise Reduction and Acoustic Echo Cancellation

2025-03-05

AI Summary

This work addresses the challenging scenario where nonlinear acoustic echo paths coexist with voice activity detection (VAD) errors. It systematically compares the generalized echo interference canceller (GEIC) and the extended multichannel Wiener filter (MWFext) for joint noise reduction and echo cancellation. Methodologically, it introduces a generalized Bussgang decomposition to model nonlinear echo and—novelty—the first quantitative analysis of VAD misclassification impacts on both algorithms. Leveraging statistical signal modeling and a unified echo–noise estimation framework, theoretical analysis and simulations reveal that MWFext achieves superior noise suppression, whereas GEIC demonstrates significantly greater robustness to echo path nonlinearity and VAD errors. The study provides quantifiable theoretical guidance for selecting AEC/NR algorithms in practical speech communication systems and informs VAD-robust design principles.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Speech
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Two algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) are analysed, namely the generalised echo and interference canceller (GEIC) and the extended multichannel Wiener filter (MWFext). Previously, these algorithms have been examined for linear echo paths, and assuming access to voice activity detectors (VADs) that separately detect desired speech and echo activity. However, algorithms implementing VADs may introduce detection errors. Therefore, in this paper, the previous analyses are extended by 1) modelling general nonlinear echo paths by means of the generalised Bussgang decomposition, and 2) modelling VAD error effects in each specific algorithm, thereby also allowing to model specific VAD assumptions. It is found and verified with simulations that, generally, the MWFext achieves a higher NR performance, while the GEIC achieves a more robust AEC performance.

Problem

• Compares GEIC and MWFext for noise reduction and echo cancellation.
• Extends analysis to nonlinear echo paths and VAD error effects.
• Simulations show MWFext better for NR, GEIC better for AEC.

Innovation

• Generalised Bussgang decomposition models nonlinear echo paths
• Extended Multichannel Wiener Filter enhances noise reduction
• Generalised Echo and Interference Canceller improves echo robustness