A BAYESIAN NETWORK VIEW ON LINEAR AND NONLINEAR ACOUSTIC ECHO CANCELLATION

被引：0

作者：

Maas, Roland ^{[1
]}

Huemmer, Christian ^{[1
]}

Schwarz, Andreas ^{[1
]}

Hofmann, Christian ^{[1
]}

Kellermann, Walter ^{[1
]}

机构：

[1] Univ Erlangen Nurnberg, Multimedia Commun & Signal Proc, D-91054 Erlangen, Germany

来源：

2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (CHINASIP) | 2014年

关键词：

machine learning for signal processing; Bayesian networks; system identification; adaptive filtering; nonlinear acoustic echo cancellation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this contribution, we provide a new derivation of the normalized least mean square (NLMS) algorithm from a machine learning perspective. By applying the inference rules of Bayesian networks to a linear observation model, the NLMS can be shown to arise as a modification of the Kalman filter equations. Based on a nonlinear observation model, we exemplify the benefit of the Bayesian point of view by employing the technique of particle filtering to realize a tractable algorithm for nonlinear acoustic echo cancellation. Experiments carried out on real smartphone recordings reveal the remarkable performance of the new approach.

引用

页码：495 / 499

页数：5

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