Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis

TL;DR

This paper presents a novel sub-band multi-hypothesis acoustic echo canceler that extracts discriminative features for acoustic scene analysis, effectively handling challenging scenarios like double-talk and echo path changes.

Contribution

It introduces a new SDMH-AEC-based feature extraction method that leverages multiple adaptive filters for improved acoustic scene analysis.

Findings

Effective in real data with double-talk and echo path changes

Enables acoustic scene analysis using existing echo cancellation algorithms

Provides rich, low-cost features from filter statistics

Abstract

This paper introduces a novel approach for acoustic scene analysis by exploiting an ensemble of statistics extracted from a sub-band domain multi-hypothesis acoustic echo canceler (SDMH-AEC). A well-designed SDMH-AEC employs multiple adaptive filtering strategies with potentially complementary behaviours during convergence, perturbations, and steady-state conditions. By aggregating statistics across the sub-bands, we derive a feature vector that exhibits strong discriminative power for distinguishing different acoustic events and estimating acoustic parameters. The complementary nature of the SDMH-AEC filters provides a rich source of information that can be extracted at insignificant cost for acoustic scene analysis tasks. We demonstrate the effectiveness of the proposed approach experimentally with real data containing double-talk, echo path change and events where the full-duplex device is physically moved. The extracted features enable acoustic scene analysis using existing echo cancellation algorithms and techniques.