Heavy-tailed Distributions-Based Online Semi-blind Source Separation for Nonlinear Echo Cancellation

Liyuan Zhang, Xianrui Wang, Yichen Yang, Tetsuya Ueda, Shoji Makino, Jingdong Chen

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

Abstract

Recently proposed semi-blind source separation (SBSS) based acoustic echo cancellation (AEC) algorithms have attracted significant research interest due to their ability to track dynamic acoustic environments in the presence of near-end signals. Two source models are considered in these existing algorithms, i.e., the spherical generalized super-Gaussian distribution and the circular super-Gaussian distribution with a low-rank spectrogram model. In this paper, we aim to further enhance AEC performance by leveraging more flexible source models. Several novel algorithms are subsequently proposed. Simulations demonstrate the superiority of proposed algorithms in various situations.

Original languageEnglish
Title of host publicationAPSIPA ASC 2024 - Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350367331
DOIs
StatePublished - 2024
Event2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2024 - Macau, China
Duration: 3 Dec 20246 Dec 2024

Publication series

NameAPSIPA ASC 2024 - Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2024

Conference

Conference2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2024
Country/TerritoryChina
CityMacau
Period3/12/246/12/24

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