Infinite impulse response echo canceller in STFT domain for reverberant environments

Amos Schreibman, Shmulik Markovich-Golan

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

Abstract

Acoustic echo cancellation and system identification in reverberant environments have been thoroughly studied in the literature. Theoretically, in a reverberant environment the Acoustic Impulse Response (AIR) relating the loudspeaker signal, denoted reference, with the corresponding signal component at the microphone, denoted echo, is of an infinite length and can be modeled as an Infinite Impulse Response (IIR) filter. Correspondingly, the echo signal can be modeled as an Auto Regressive Moving Average (ARMA) process. Yet, most methods for this problem adopt a Finite Impulse Response (FIR) system model or equivalently a Moving Average (MA) echo signal model due to their favorable simplicity and stability. Latter methods, denoted FIR-Acoustic Echo Canceller (AEC), employ an Adaptive Filter (AF) for tracking a possibly time-varying system and cancelling echo. Some contributions adopt an IIR system model and utilize it to derive a time-domain AEC and accurately analyze the room behaviour. An IIR system model has also been successfully applied in the Short Time Fourier Transform (STFT) domain for the dereverberation problem. In this contribution we consider an IIR model in the STFT domain and propose a novel online AEC algorithm, denoted IIR-AEC, which tracks the model parameters and cancels echo. The order of the feed-back filter, equivalent to the order of the Auto Regressive (AR) part of the echo signal model, can be designed to fit the acoustic model and the order of the feed-forward filter, equivalent to the order of the MA part of the echo signal model, is limited to a single tap, thereby requiring that the STFT window is longer than the early part of the AIR. The computational complexity of proposed IIR-AEC is comparable to a Recursive Least Squares (RLS) implementation of FIR-AEC. These methods are evaluated using real measured AIRs drawn from a recording campaign and the IIR-AEC is shown to outperform the FIR-AEC.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
StatePublished - Sep 2019
Externally publishedYes
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
Country/TerritorySpain
CityA Coruna
Period2/09/196/09/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE

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