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Deep Neural Network Based Regression Approach for Acoustic Echo Cancellation

Authors:

Lirong DaiAuthors Info & Claims

ICMSSP '19: Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing

Pages 94 - 98

https://doi.org/10.1145/3330393.3330399

Published: 10 May 2019 Publication History

Abstract

An acoustic echo canceller (AEC) aims to remove the acoustic echo in the mixture signal received by the near-end microphone. The conventional method uses an adaptive finite impulse response (FIR) filter to identify a room impulse response (RIR)which is not robust to various wild scenarios. In this paper, we propose a deep neural network-based regression approach that directly estimates the amplitude spectrum of the near-end target signal from features extracted from the mixtures of near-end and far-end signals. Depend on the powerful modelling and generalizing ability of deep learning, the complex echo signal can be well eliminated. Experimental results show the effectiveness of the proposed method for echo removal in double-talk, background noise, RIR variation and nonlinear distortion scenarios. In addition, the proposed method generalizes well to real-life acoustic echoes recorded in vehicles.

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Cited By

Cornell SBalestri TSenechal T(2023)Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection2022 IEEE Spoken Language Technology Workshop (SLT)10.1109/SLT54892.2023.10022358(1052-1058)Online publication date: 9-Jan-2023
https://doi.org/10.1109/SLT54892.2023.10022358
Zheng CZhou YPeng XZhang YLu Y(2022)Time-Variance Aware Dynamic Kernel Generation for Real-Time Acoustic Echo CancellationIEEE Signal Processing Letters10.1109/LSP.2022.316435929(967-971)Online publication date: 2022
https://doi.org/10.1109/LSP.2022.3164359
Ivry ACohen IBerdugo B(2021)Deep Residual Echo Suppression With A Tunable Tradeoff Between Signal Distortion And Echo SuppressionICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414958(126-130)Online publication date: 6-Jun-2021
https://doi.org/10.1109/ICASSP39728.2021.9414958
Show More Cited By

Index Terms

Deep Neural Network Based Regression Approach for Acoustic Echo Cancellation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition

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cover image ACM Other conferences

ICMSSP '19: Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing

May 2019

213 pages

ISBN:9781450371711

DOI:10.1145/3330393

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University
Sun Yat-Sen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 May 2019

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ICMSSP 2019

ICMSSP 2019: 2019 4th International Conference on Multimedia Systems and Signal Processing

May 10 - 12, 2019

Guangzhou, China

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Cited By

Cornell SBalestri TSenechal T(2023)Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection2022 IEEE Spoken Language Technology Workshop (SLT)10.1109/SLT54892.2023.10022358(1052-1058)Online publication date: 9-Jan-2023
https://doi.org/10.1109/SLT54892.2023.10022358
Zheng CZhou YPeng XZhang YLu Y(2022)Time-Variance Aware Dynamic Kernel Generation for Real-Time Acoustic Echo CancellationIEEE Signal Processing Letters10.1109/LSP.2022.316435929(967-971)Online publication date: 2022
https://doi.org/10.1109/LSP.2022.3164359
Ivry ACohen IBerdugo B(2021)Deep Residual Echo Suppression With A Tunable Tradeoff Between Signal Distortion And Echo SuppressionICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414958(126-130)Online publication date: 6-Jun-2021
https://doi.org/10.1109/ICASSP39728.2021.9414958
Peng RCheng LZheng CLi X(2021)ICASSP 2021 Acoustic Echo Cancellation Challenge: Integrated Adaptive Echo Cancellation with Time Alignment and Deep Learning-Based Residual Echo Plus Noise SuppressionICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414462(146-150)Online publication date: 6-Jun-2021
https://doi.org/10.1109/ICASSP39728.2021.9414462
Howard NPark AShabestary TGruenstein APrabhavalkar R(2021)A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer and Large Scale Synthetic DataICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9413585(7128-7132)Online publication date: 6-Jun-2021
https://doi.org/10.1109/ICASSP39728.2021.9413585
Ding SJia YHu KWang Q(2021)Textual Echo Cancellation2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU51503.2021.9688214(548-555)Online publication date: 13-Dec-2021
https://doi.org/10.1109/ASRU51503.2021.9688214
O'Malley TNarayanan AWang QPark AWalker JHoward N(2021)A Conformer-Based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU51503.2021.9687942(304-311)Online publication date: 13-Dec-2021
https://doi.org/10.1109/ASRU51503.2021.9687942

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