Nonlinear Acoustic Echo Cancellation with Deep Learning

TL;DR

This paper introduces a neural network-based nonlinear acoustic echo cancellation system that models device-induced distortions and is optimized jointly with an adaptive filter, suitable for embedded hands-free devices.

Contribution

It presents a novel neural network architecture with trainable memory and nonlinear activation functions, jointly optimized with a linear filter for improved echo cancellation.

Findings

Outperforms competing methods on real and synthetic data

Operates efficiently on standard neural processors

Requires only 17,000 parameters and meets timing constraints

Abstract

We propose a nonlinear acoustic echo cancellation system, which aims to model the echo path from the far-end signal to the near-end microphone in two parts. Inspired by the physical behavior of modern hands-free devices, we first introduce a novel neural network architecture that is specifically designed to model the nonlinear distortions these devices induce between receiving and playing the far-end signal. To account for variations between devices, we construct this network with trainable memory length and nonlinear activation functions that are not parameterized in advance, but are rather optimized during the training stage using the training data. Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone. During training, the network and filter are jointly optimized to learn the network parameters. This system requires 17 thousand parameters that consume 500 Million floating-point operations per second and 40 Kilo-bytes of memory. It also satisfies hands-free communication timing requirements on a standard neural processor, which renders it adequate for embedding on hands-free communication devices. Using 280 hours of real and synthetic data, experiments show advantageous performance compared to competing methods.