Deep Adaptation Control for Stereophonic Acoustic Echo Cancellation

Amir Ivry, Israel Cohen, Baruch Berdugo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce a general and data-driven adaptation-control framework for stereophonic acoustic-echo cancellation. The adaptation update rule for the filters that estimate the actual echo paths is compactly expressed with the widely-linear model in the complex time domain. A single step-size parameter that governs the behavior of the adaptation process is optimized by minimizing the misalignment between the actual echo paths and their filtered estimate. The relation between acoustic signals and the optimal step-size is learned via a deep neural network. In test mode, the optimal step-size prediction is inferred by the network and fed to the sign-error nor-malized least mean-squares (SNLMS) adaptive filter for echo-paths tracking. Real and simulated data show advantageous performance in single and double-talk scenarios across various acoustic setups.

Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323726
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023 - New Paltz, United States
Duration: 22 Oct 202325 Oct 2023

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Volume2023-October
ISSN (Print)1931-1168
ISSN (Electronic)1947-1629

Conference

Conference2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2023
Country/TerritoryUnited States
CityNew Paltz
Period22/10/2325/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Stereophonic acoustic echo cancellation
  • adaptation control
  • deep learning
  • sign-error NLMS
  • variable step-size

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