Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise

TL;DR

This paper introduces a neural network-based multichannel approach for the simultaneous reduction of acoustic echo, reverberation, and noise, outperforming traditional cascade and joint methods in real-world recordings.

Contribution

It proposes a novel joint spectral modeling framework with an iterative algorithm for optimizing multiple distortion-specific filters simultaneously.

Findings

Outperforms cascade and joint approaches in real recordings

Effective multichannel Gaussian spectral modeling

Iterative algorithm improves filter optimization

Abstract

We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. We propose to model the target and residual signals after linear echo cancellation and dereverberation using a multichannel Gaussian modeling framework and to jointly represent their spectra by means of a neural network. We develop an iterative block-coordinate ascent algorithm to update all the filters. We evaluate our system on real recordings of acoustic echo, reverberation and noise acquired with a smart speaker in various situations. The proposed approach outperforms in terms of overall distortion a cascade of the individual approaches and a joint reduction approach which does not rely on a spectral model of the target and residual signals.