A novel recurrent network for independent component analysis of post nonlinear convolutive mixtures

被引：0

作者：

Vigliano, D ^{[1
]}

Parisi, R ^{[1
]}

Uncini, A ^{[1
]}

机构：

[1] Univ Roma La Sapienza, Dipartimento INFOCOM, I-00184 Rome, Italy

来源：

2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS: DESIGN AND IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS INDUSTRY TECHNOLOGY TRACKS MACHINE LEARNING FOR SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING SIGNAL PROCESSING FOR EDUCATION | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a novel Independent Component Analysis approach to the separation of nonlinear convolutive mixtures. In particular, convolutive mixing of post nonlinear mixtures is considered. Source separation is performed by a new efficient recurrent network, which is able to ensure faster training with respect to currently available feedforward architectures, with lower computational costs. The proposed architecture makes proper use of flexible spline neurons for on-line estimation of the score function. Experimental results are described to demonstrate the effectiveness of the proposed technique.

引用

页码：553 / 556

页数：4

共 14 条

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