Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation

Yiu ming Cheung; Lei Xu

doi:10.1109/IJCNN.1999.831083

Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation

Yiu ming Cheung
, Lei Xu

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Most existing independent component analysis (ICA) approaches are proposed for blind signal separation under the assumption that the sources are independently and identically distributed (i.i.d.) signals. However, the real signals are often temporal correlated in a certain degree. In our paper, we have presented an Auto-regressive Signal Separation Approach (ASSA) for AR(p) temporal signal separation, where we assume the noises in AR source signals are non-Gaussian. In this paper, we further study this approach under the Gaussian noises in AR sources with providing a seesaw-mapping technique. Experiments have demonstrated that the seesaw-mapping technique can make ASSA approach applied to this case successfully.

Original language	English
Title of host publication	IJCNN'99. International Joint Conference on Neural Networks. Proceedings
Publisher	IEEE
Pages	961-964
Number of pages	4
ISBN (Print)	0780355296
DOIs	https://doi.org/10.1109/IJCNN.1999.831083
Publication status	Published - 10 Jul 1999
Event	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: 10 Jul 1999 → 16 Jul 1999

Publication series

Name	International Joint Conference on Neural Networks - Proceedings
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'99)
City	Washington, DC, USA
Period	10/07/99 → 16/07/99

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10.1109/IJCNN.1999.831083

Cite this

@inproceedings{b1150ec75baf41c3855ecd5d7ef55b0b,

title = "Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation",

abstract = "Most existing independent component analysis (ICA) approaches are proposed for blind signal separation under the assumption that the sources are independently and identically distributed (i.i.d.) signals. However, the real signals are often temporal correlated in a certain degree. In our paper, we have presented an Auto-regressive Signal Separation Approach (ASSA) for AR(p) temporal signal separation, where we assume the noises in AR source signals are non-Gaussian. In this paper, we further study this approach under the Gaussian noises in AR sources with providing a seesaw-mapping technique. Experiments have demonstrated that the seesaw-mapping technique can make ASSA approach applied to this case successfully.",

author = "Cheung, \{Yiu ming\} and Lei Xu",

note = "Publisher Copyright: {\textcopyright} 1999 IEEE; International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

year = "1999",

month = jul,

day = "10",

doi = "10.1109/IJCNN.1999.831083",

language = "English",

isbn = "0780355296",

series = "International Joint Conference on Neural Networks - Proceedings",

publisher = "IEEE",

pages = "961--964",

booktitle = "IJCNN'99. International Joint Conference on Neural Networks. Proceedings",

address = "United States",

}

Cheung, YM & Xu, L 1999, Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation. in IJCNN'99. International Joint Conference on Neural Networks. Proceedings. International Joint Conference on Neural Networks - Proceedings, IEEE, pp. 961-964, International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, 10/07/99. https://doi.org/10.1109/IJCNN.1999.831083

Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation. / Cheung, Yiu ming; Xu, Lei.
IJCNN'99. International Joint Conference on Neural Networks. Proceedings. IEEE, 1999. p. 961-964 (International Joint Conference on Neural Networks - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation

AU - Cheung, Yiu ming

AU - Xu, Lei

PY - 1999/7/10

Y1 - 1999/7/10

N2 - Most existing independent component analysis (ICA) approaches are proposed for blind signal separation under the assumption that the sources are independently and identically distributed (i.i.d.) signals. However, the real signals are often temporal correlated in a certain degree. In our paper, we have presented an Auto-regressive Signal Separation Approach (ASSA) for AR(p) temporal signal separation, where we assume the noises in AR source signals are non-Gaussian. In this paper, we further study this approach under the Gaussian noises in AR sources with providing a seesaw-mapping technique. Experiments have demonstrated that the seesaw-mapping technique can make ASSA approach applied to this case successfully.

AB - Most existing independent component analysis (ICA) approaches are proposed for blind signal separation under the assumption that the sources are independently and identically distributed (i.i.d.) signals. However, the real signals are often temporal correlated in a certain degree. In our paper, we have presented an Auto-regressive Signal Separation Approach (ASSA) for AR(p) temporal signal separation, where we assume the noises in AR source signals are non-Gaussian. In this paper, we further study this approach under the Gaussian noises in AR sources with providing a seesaw-mapping technique. Experiments have demonstrated that the seesaw-mapping technique can make ASSA approach applied to this case successfully.

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U2 - 10.1109/IJCNN.1999.831083

DO - 10.1109/IJCNN.1999.831083

M3 - Conference proceeding

AN - SCOPUS:0033346035

SN - 0780355296

T3 - International Joint Conference on Neural Networks - Proceedings

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BT - IJCNN'99. International Joint Conference on Neural Networks. Proceedings

PB - IEEE

T2 - International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

Auto-regressive Signal Separation Approach with seesaw-mapping technique on temporal source separation

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