Nonlinear acoustic echo canceller with DABNet + FIR structure

Sentoni, G.; Altenberg, A. E.

doi:10.1109/aspaa.2005.1540162

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…In [100], an RLS-type adaptation is derived to speed up the convergence of the polynomial. Moreover, some methods propose more efficient AEC designs in terms of both nonlinear models and filter adaptation schemes, for example, the Volterra model-based [38,59] and neural network structure-based [83] approaches.…”

Section: Nonlinear Acoustic Echo Canceller (Naec)mentioning

confidence: 99%

Nonlinear Acoustic Echo Cancellation

Shi¹

2006

Topics in Acoustic Echo and Noise Control

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Section: Nonlinear Acoustic Echo Canceller (Naec)mentioning

confidence: 99%

Nonlinear Acoustic Echo Cancellation

Shi¹

2006

Topics in Acoustic Echo and Noise Control

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“…Although nonlinear residual echo suppression (NL-RES) can be applied to improve the performance of linear echo cancelers [12], [13], the unavoidable distortions of the near-end signal caused by NL-RES and the limited practical performance of linear echo path models fuel the increasing interest in NL-AEC [14]. A variety of concepts for NL-AEC have been proposed based on Volterra filters [15]- [17], artificial neural networks (ANNs) [18]- [20], functional link adaptive filters (FLAFs) [21] or Kernel methods [22]- [24]. An additional approach for modeling the nonlinear echo path is a nonlinear-linear cascade of memoryless loudspeaker signal preprocessing (defined by a set of preprocessor coefficients) and linear adaptive filtering (to model the acoustic wave propagation).…”

Section: Introductionmentioning

confidence: 99%

The significance-aware EPFES to estimate a memoryless preprocessor for nonlinear acoustic echo cancellation

Huemmer

Hofmann

Maas

et al. 2014

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In this article, we introduce a novel approach for estimating the coefficients of a memoryless preprocessor for nonlinear acoustic echo cancellation (NL-AEC) using particle filtering. The acoustic echo path is modeled by a nonlinearlinear cascade of a memoryless preprocessor (to model the loudspeaker nonlinearities) preceding a linear finite impulse response filter (estimated by the normalized least mean square algorithm). For identifying the loudspeaker signal distortions, we follow the concept of significance-aware filtering by modeling the time-variant coefficients of the memoryless preprocessor and the direct-path part of the room impulse response vector as one state vector with non-Gaussian probability distribution. Due to the nonlinear relation between the state vector and the observation, we propose a computationally-efficient realization of the recently published elitist particle filter based on evolutionary strategies (EPFES), which evaluates realizations of the state vector based on long-term fitness measures. The experimental validation comprises predefined loudspeaker signal distortions as well as real recordings stemming from a commercial smartphone. In comparison to the well-known Hammerstein group model for NL-AEC, the computational complexity is reduced and the achievable system identification is improved for both scenarios.

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Nonlinear Acoustic Echo Canceller to Combat Sigmoid-Type Nonlinearities Under Noisy Environment

Kohli

Sharma

2020

Wireless Pers Commun

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Nonlinear acoustic echo canceller with DABNet + FIR structure

Cited by 5 publications

References 6 publications

Nonlinear Acoustic Echo Cancellation

Nonlinear Acoustic Echo Cancellation

The significance-aware EPFES to estimate a memoryless preprocessor for nonlinear acoustic echo cancellation

Nonlinear Acoustic Echo Canceller to Combat Sigmoid-Type Nonlinearities Under Noisy Environment

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