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Echo Cancellation

Multiple-input neural network-based residual echo suppression


A residual echo suppressor (RES) aims to suppress the residual echo in the output of an acoustic echo canceler (AEC). Spectral-based RES approaches typically estimate the magnitude spectra of the near-end speech and the residual echo from a single input, that is either the far-end speech or the echo computed by the AEC, and derive the RES filter coefficients accordingly. These single inputs do not always suffice to discriminate the near-end speech from the remaining echo.

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Authors:
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert
Submitted On:
25 April 2018 - 5:13am
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posterICASSP_CARBAJAL.pdf

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[1] Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert, "Multiple-input neural network-based residual echo suppression", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3178. Accessed: Jul. 19, 2018.
@article{3178-18,
url = {http://sigport.org/3178},
author = {Guillaume Carbajal; Romain Serizel; Emmanuel Vincent; Eric Humbert },
publisher = {IEEE SigPort},
title = {Multiple-input neural network-based residual echo suppression},
year = {2018} }
TY - EJOUR
T1 - Multiple-input neural network-based residual echo suppression
AU - Guillaume Carbajal; Romain Serizel; Emmanuel Vincent; Eric Humbert
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3178
ER -
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. (2018). Multiple-input neural network-based residual echo suppression. IEEE SigPort. http://sigport.org/3178
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert, 2018. Multiple-input neural network-based residual echo suppression. Available at: http://sigport.org/3178.
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. (2018). "Multiple-input neural network-based residual echo suppression." Web.
1. Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. Multiple-input neural network-based residual echo suppression [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3178

NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER


This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probability distribution of the state vector, an elitist particle filter based on evolutionary strategies (EPFES) has been proposed, which evaluates realizations of the latent state vector based on long-term fitness measures.

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Authors:
MHD Modar Halimeh, Christian Huemmer, Walter Kellermann
Submitted On:
26 April 2018 - 3:21am
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Nonlinear_Acoustic_Echo_Cancellation_Using_Elitist_Particle_Filter_Poster.pdf

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[1] MHD Modar Halimeh, Christian Huemmer, Walter Kellermann, "NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2644. Accessed: Jul. 19, 2018.
@article{2644-18,
url = {http://sigport.org/2644},
author = {MHD Modar Halimeh; Christian Huemmer; Walter Kellermann },
publisher = {IEEE SigPort},
title = {NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER},
year = {2018} }
TY - EJOUR
T1 - NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER
AU - MHD Modar Halimeh; Christian Huemmer; Walter Kellermann
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2644
ER -
MHD Modar Halimeh, Christian Huemmer, Walter Kellermann. (2018). NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER. IEEE SigPort. http://sigport.org/2644
MHD Modar Halimeh, Christian Huemmer, Walter Kellermann, 2018. NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER. Available at: http://sigport.org/2644.
MHD Modar Halimeh, Christian Huemmer, Walter Kellermann. (2018). "NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER." Web.
1. MHD Modar Halimeh, Christian Huemmer, Walter Kellermann. NONLINEAR ACOUSTIC ECHO CANCELLATION USING ELITIST RESAMPLING PARTICLE FILTER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2644

An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation

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Authors:
Zhiping Zhang, Zhiqiang Wu
Submitted On:
13 October 2016 - 10:53pm
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Parametric adaptive filter

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[1] Zhiping Zhang, Zhiqiang Wu, "An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1178. Accessed: Jul. 19, 2018.
@article{1178-16,
url = {http://sigport.org/1178},
author = {Zhiping Zhang; Zhiqiang Wu },
publisher = {IEEE SigPort},
title = {An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation},
year = {2016} }
TY - EJOUR
T1 - An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation
AU - Zhiping Zhang; Zhiqiang Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1178
ER -
Zhiping Zhang, Zhiqiang Wu. (2016). An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation. IEEE SigPort. http://sigport.org/1178
Zhiping Zhang, Zhiqiang Wu, 2016. An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation. Available at: http://sigport.org/1178.
Zhiping Zhang, Zhiqiang Wu. (2016). "An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation." Web.
1. Zhiping Zhang, Zhiqiang Wu. An Adaptive Filter with Gain and Time-shift Parameters for Echo Cancellation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1178

The recursive Hessian sketch for adaptive filtering


The recursive Hessian sketch for adaptive filtering as a block diagram

We introduce in this paper the recursive Hessian sketch, a new adaptive filtering algorithm based on sketching the same exponentially weighted least squares problem solved by the recursive least squares algorithm. The algorithm maintains a number of sketches of the inverse autocorrelation matrix and recursively updates them at random intervals. These are in turn used to update the unknown filter estimate. The complexity of the proposed algorithm compares favorably to that of recursive least squares.

Paper Details

Authors:
Robin Scheibler, Martin Vetterli
Submitted On:
29 March 2016 - 4:50am
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icassp2016_slides.pdf

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[1] Robin Scheibler, Martin Vetterli, "The recursive Hessian sketch for adaptive filtering", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1064. Accessed: Jul. 19, 2018.
@article{1064-16,
url = {http://sigport.org/1064},
author = {Robin Scheibler; Martin Vetterli },
publisher = {IEEE SigPort},
title = {The recursive Hessian sketch for adaptive filtering},
year = {2016} }
TY - EJOUR
T1 - The recursive Hessian sketch for adaptive filtering
AU - Robin Scheibler; Martin Vetterli
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1064
ER -
Robin Scheibler, Martin Vetterli. (2016). The recursive Hessian sketch for adaptive filtering. IEEE SigPort. http://sigport.org/1064
Robin Scheibler, Martin Vetterli, 2016. The recursive Hessian sketch for adaptive filtering. Available at: http://sigport.org/1064.
Robin Scheibler, Martin Vetterli. (2016). "The recursive Hessian sketch for adaptive filtering." Web.
1. Robin Scheibler, Martin Vetterli. The recursive Hessian sketch for adaptive filtering [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1064