A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

Authors:
Akihide Horita;Kenji Nakayama;Akihiro Hirano
Affiliations:
-;-;-
Venue:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Year:
2007

Citing 6
Cited 0

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ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05

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Abstract

FeedForward (FF-) Blind Source Separation (BSS) systems have some degree of freedom in the solution space. Therefore, signal distortion is likely to occur. First, a criterion for the signal distortion is discussed. Properties of conventional methods proposed to suppress the signal distortion are analyzed. Next, a general condition for complete separation and distortion-free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion-free constraint. Computer simulations using speech signals and stationary colored signals are performed for the conventional methods and for the new learning algorithms employing the proposed distortion-free constraint. The proposed method can well suppress signal distortion, while maintaining a high source separation performance.