@inproceedings{3517c56b3c1d41e3924097d10e00b0b9,
title = "DEEP ADAPTATION CONTROL FOR ACOUSTIC ECHO CANCELLATION",
abstract = "We propose a general framework for adaptation control using deep neural networks (NNs) and apply it to acoustic echo cancellation (AEC). First, the optimal step-size that controls the adaptation is derived offline by solving a constrained nonlinear optimization problem that minimizes the adaptive filter misadjustment. Then, a deep NN is trained to learn the relation between the input data and the optimal step-size. In real-time, the NN infers the optimal step-size from streaming data and feeds it to an NLMS filter for AEC. This data-driven method makes no assumptions on the acoustic setup and is entirely non-parametric. Experiments with 100 h of real and synthetic data show that the proposed method outperforms the competition in echo cancellation, speech distortion, and convergence during both single-talk and double-talk.",
keywords = "Acoustic echo cancellation, adaptation control, deep learning, double-talk, variable step-size",
author = "Amir Ivry and Israel Cohen and Baruch Berdugo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
doi = "https://doi.org/10.1109/ICASSP43922.2022.9746557",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "741--745",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
address = "الولايات المتّحدة",
}