Nonlinear Acoustic Echo Cancellation Using a Nonlinear Postprocessor With a Linearly Constrained Affine Projection Algorithm

This brief presents a nonlinear acoustic echo cancellation algorithm comprising a postprocessor that can model the saturation-type nonlinear distortion of a microphone circuit. To support fast adaptation to the acoustic echo environment, the linear filter is adapted using an affine projection algori...

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Vydané v:IEEE transactions on circuits and systems. II, Express briefs Ročník 62; číslo 9; s. 881 - 885
Hlavní autori: Lee, Keunsang, Baek, Yonghyun, Park, Youngcheol
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acoustic distortion
Acoustics
Algorithms
Cancellation
Circuits
Convergence
Distortion
Echo cancellers
hard-clipper function
linear constraint affine projection
Mathematical analysis
Microphones
Nonlinear acoustic echo cancellation
Nonlinear distortion
nonlinear postprocessor
Nonlinearity
Projection
sigmoid function
Signal processing algorithms
Vectors (mathematics)
Hard-clipper function
nonlinear postprocessor
nonlinear acoustic echo cancellation (NAEC)
sigmoid function
linear constraint affine projection (AP)
ISSN:1549-7747, 1558-3791
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Popis
Shrnutí:This brief presents a nonlinear acoustic echo cancellation algorithm comprising a postprocessor that can model the saturation-type nonlinear distortion of a microphone circuit. To support fast adaptation to the acoustic echo environment, the linear filter is adapted using an affine projection algorithm where the reference input vectors subject to linear operation are dynamically selected to update the coefficient vector. We consider the hard-clipper and sigmoid functions for the postprocessor, and their parameters are adaptively updated using the least-mean-square algorithm. The simulation results confirm that the proposed algorithm achieves fast and stable convergence under saturation-type microphone distortion, and it can have better performance than conventional algorithms.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2015.2435711