Nonlinear System Identification Using a Subband Adaptive Volterra Filter

Burton, Trevor; Beaucoup, Franck; Goubran, Rafik

doi:10.1109/imtc.2008.4547171

Cited by 12 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output of the system is contaminated by zero-mean Gaussian white noise. The channel impulse response considered in this study is given by [34][35][36] 5000 samples and carry out 200 independent runs to get stable values of MSE. In this simulation, four different channels were studied with the following normalized transfer function given in z-transform form:…”

Section: Nonlinear Channel Equalizationmentioning

confidence: 99%

“…Therefore, Volterra filters have been widely used in many fields such as nonlinear system identification [1][2][3], speech prediction [4,5], channel equalizer [6], acoustic echo cancellation [7], and image processing [8]. However, a major problem in the implementation of Volterra filters is their heavy computational complexity, which is caused by the large number of filter coefficients rising geometrically with the orders and memory depth (or delays).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

Pang

Zhang

2016

Digital Signal Processing

View full text Add to dashboard Cite

Section: Nonlinear Channel Equalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

Pang

Zhang

2016

Digital Signal Processing

View full text Add to dashboard Cite

“…The spectral content change (SCC) is defined as the relative change in power observed in the specified bands (from f 1 to f 2 ) [33], including delta (B3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30):…”

Section: Performance Assessmentmentioning

confidence: 99%

“…Wiener worked on the analysis of nonlinear systems using white Gaussian input and so-called G functions [25]. Following his work, many researchers have utilized the Volterra series for the estimation and identification of systems and Volterra models have been applied and become very popular recently [26][27][28][29]. The present study develops a VF method based on a multichannel structure for removing ocular artefacts.…”

Section: Introductionmentioning

confidence: 99%

Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals

et al. 2015

View full text Add to dashboard Cite

Ocular artefacts are the most important form of interference in electroencephalography (EEG) signals. These eye movements have high amplitude and a frequency band that overlaps that of physiological or brain signals. It is important to remove these ocular artefacts before analyzing EEG signals to obtain accurate information about brain activity and avoid mistakes in its interpretation. This study presents an ocular artefact removal method based on a Volterra filter (VF) with a multichannel structure. Based on applications with various real and synthetic signals, the accuracy of the VF is compared with those of existing methods based on principal component analysis, support vector machines, and independent component analysis. The VF method has lower values of normalized mean square error, correlation coefficient, and spectral content change, and higher eye reduction, providing a good trade-off between removing artefacts and preserving inherent brain activities. The VF method can thus serve as an effective framework for substantially removing eye interference in EEG recordings.

show abstract

“…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%

A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

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.

show abstract

Nonlinear System Identification Using a Subband Adaptive Volterra Filter

Cited by 12 publications

References 14 publications

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals

A second-order Volterra filter-based nonlinear clipping detector

Contact Info

Product

Resources

About