NDLI: learning algorithm with distortion free constraint and comparative study for feedforward and feedback BSS”, EUSIPCO2006

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learning algorithm with distortion free constraint and comparative study for feedforward and feedback BSS”, EUSIPCO2006

Content Provider	CiteSeerX
Author	Horita, Akihide Hirano, Akihiro Nakayama, Kenji Dejima, Yasuhiro
Abstract	In Blind Source Separation (BSS), a separation block is trained so as to make the output signals to be statis-tically independent. In this case, the independency is able to be increased by changing frequency response of the output signals, resulting in signal distortion. Es-pecially, a feed-forward BSS (FF-BSS) has some de-gree of freedom in the separation block, and the sig-nal distortion will be caused. The signal distortion is evaluated as difference between the output signal and the signal source in the measured signal. Some equations are derived from the conditions of complete separation and signal distortion free. They are used as the distortion free constraint in the conventional learn-ing process [11]. On the other hand, a feedback BSS (FB-BSS) has a solution, which can satisfy both com-plete separation and distortion free. In this paper, the learning algorithm with the distortion free constraint is applied to the FF-BSS in time domain. Many kinds of signal sources are used in simulation in order to compare the proposed method and the conventional, in which difference between the output signals and the measured signals is included in the cost function [4]. Furthermore, the FB-BSS is also evaluated. (BSS) BSS(FF-BSS) [11] si xi yi yi xi si
File Format	PDF
Access Restriction	Open
Subject Keyword	Com-plete Separation Measured Signal Complete Separation Distortion Free Constraint Feed-forward Bs Separation Block Signal Source Conventional Learn-ing Process Signal Distortion Sig-nal Distortion Feedback Bs
Content Type	Text

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