Nonlinear acoustic echo cancellation based on volterra filters

Guérin, Alexandre; Faucon, G.; Jeannès, Régine Le Bouquin

doi:10.1109/tsa.2003.818077

Cited by 131 publications

(63 citation statements)

References 8 publications

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“…The Volterra filter is originally identical to a generalized Taylor series representation of a function with memory; hence, the Volterra filter is able to characterize a wide range of nonlinear systems [11]. Specifically, the second-order Volterra filter consists of linear and quadratic filters, and its output can be represented as …”

Section: Proposed Nonlinear Clipping Detector For Aecmentioning

confidence: 99%

“…In this paper, we present a nonlinear clipping detector to identify nonnegligible nonlinear distortion periods using a portion of the second-order Volterra filter [9][10][11], which efficiently characterizes speaker distortion [12,13]. Thus, the nonlinear clipping detector pauses the linear adaptive filter activity during the nonlinear clipping period so that the linear filter is updated only for the linear echo signal, which is the first approach of detecting nonlinear clipping periods without a priori clipping information.…”

Section: Introductionmentioning

confidence: 99%

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A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we propose a novel nonlinear clipping detector based on the second-order Volterra filter for an acoustic echo canceller (AEC). Since the performance of the conventional AEC algorithm drastically deteriorates when nonlinear clipping occurs, the adaptive filter pauses adaptation during the nonlinear clipping periods to avoid unwanted divergence. These nonlinear clipping periods are detected in our method using the magnitude spectrum of the quadratic Volterra filter in lower frequency ranges. Through extensive computer-based simulation results considering an acoustic room environment and various clipping levels, it is found that the proposed approach is effective in detecting the nonlinear clipping periods and improving AEC performance compared to the conventional AEC algorithm.

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Section: Proposed Nonlinear Clipping Detector For Aecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

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“…The comparative study of the proposed scheme is made with two reference solutions of second order nonlinear Volterra LMS (V-LMS) and recently developed Kernel LMS (K-LMS) algorithms. V-LMS is based on Volterra series [43] and used in many applications of nonlinear problems arising in system identification [44], active noise control [45] and acoustic echo cancellation [46]. Pokharel developed the K-LMS adaptive algorithm by exploring the application of linear LMS in kernel feature space [47].…”

Section: Introductionmentioning

confidence: 99%

Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems

Chaudhary

Raja

2015

Signal Processing

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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 cancellation based on volterra filters

Cited by 131 publications

References 8 publications

A second-order Volterra filter-based nonlinear clipping detector

A second-order Volterra filter-based nonlinear clipping detector

Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems

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

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