@inproceedings{0ba6ea955b0b4c27a081441ea362d1c2,
title = "OFF-THE-SHELF DEEP INTEGRATION FOR RESIDUAL-ECHO SUPPRESSION",
abstract = "Residual-echo suppression (RES) systems suppress the echo and preserve the speech from a mixture of the two. In handsfree speech communication, RES may also be addressed as a source separation (SS) or speech enhancement (SE) problem, where the echo can be manipulated as an interfering speech signal. In this study, we fine-tune three pre-trained deep learning-based systems originally designed for RES, SS, and SE, and show that the best performing system for the task of RES varies with respect to the acoustic conditions. Then, we propose a real-time data-driven integration of these systems, where a neural network continuously tracks the system that achieves the best performance during both single-talk and double-talk periods. Experiments with 100 h of real and synthetic data show that the integrated system outperforms each individual system in terms of echo suppression and speech distortion in various acoustic environments.",
keywords = "Acoustic echo cancellation, deep learning, residual-echo suppression, speech enhancement, speech separation",
author = "Amir Ivry and Israel Cohen and Baruch Berdugo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICASSP43922.2022.9747511",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "746--750",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
address = "الولايات المتّحدة",
}