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
收藏
页码：495 / 499
页数：5
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