NN3A: Neural Network supported Acoustic Echo Cancellation, Noise Suppression and Automatic Gain Control for Real-Time Communications

10/16/2021
by   Ziteng Wang, et al.
0

Acoustic echo cancellation (AEC), noise suppression (NS) and automatic gain control (AGC) are three often required modules for real-time communications (RTC). This paper proposes a neural network supported algorithm for RTC, namely NN3A, which incorporates an adaptive filter and a multi-task model for residual echo suppression, noise reduction and near-end speech activity detection. The proposed algorithm is shown to outperform both a method using separate models and an end-to-end alternative. It is further shown that there exists a trade-off in the model between residual suppression and near-end speech distortion, which could be balanced by a novel loss weighting function. Several practical aspects of training the joint model are also investigated to push its performance to limit.

READ FULL TEXT
research
09/29/2020

Residual acoustic echo suppression based on efficient multi-task convolutional neural network

Acoustic echo degrades the user experience in voice communication system...
research
05/13/2022

Task splitting for DNN-based acoustic echo and noise removal

Neural networks have led to tremendous performance gains for single-task...
research
10/02/2021

End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression

Echo and noise suppression is an integral part of a full-duplex communic...
research
10/30/2022

Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning

Acoustic echo cancellation (AEC) is designed to remove echoes, reverbera...
research
11/20/2019

Joint DNN-Based Multichannel Reduction of Acoustic Echo, Reverberation and Noise

We consider the problem of simultaneous reduction of acoustic echo, reve...
research
03/31/2021

Y^2-Net FCRN for Acoustic Echo and Noise Suppression

In recent years, deep neural networks (DNNs) were studied as an alternat...

Please sign up or login with your details

Forgot password? Click here to reset