A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.12 pp.2835-2845
- Publication Date
- 2007/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.12.2835
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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Akihide HORITA, Kenji NAKAYAMA, Akihiro HIRANO, "A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 12, pp. 2835-2845, December 2007, doi: 10.1093/ietfec/e90-a.12.2835.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.12.2835/_p
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@ARTICLE{e90-a_12_2835,
author={Akihide HORITA, Kenji NAKAYAMA, Akihiro HIRANO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation},
year={2007},
volume={E90-A},
number={12},
pages={2835-2845},
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.},
keywords={},
doi={10.1093/ietfec/e90-a.12.2835},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2835
EP - 2845
AU - Akihide HORITA
AU - Kenji NAKAYAMA
AU - Akihiro HIRANO
PY - 2007
DO - 10.1093/ietfec/e90-a.12.2835
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2007
AB - 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.
ER -
English
