TY - JOUR
T1 - Multi-Channel Signal Separation by Decorrelation
AU - Weinstein, Ehud
AU - Feder, Meir
AU - Oppenheim, Alan V.
N1 - Funding Information:
Manuscript received February 9, 1991; revised May 29, 1993. This work was supported in part by the Wolfson Research Awards administered by the Israel Academy of Sciences and Humanities, in part by the US. Air Force Office of Scientific Research under Grant AFOSR-91-0034, and in part by the Office of Naval Research under Grant NOOO14-91-J-1628 and Grant NOOO14- 93-1-0686. The associate editor coordinating the review of this paper and approving it for publication was Dr. B. A. Hanson. Ehud Weinstein is with the Department of Electrical Engineering-Systems, Faculty of Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel. He is also an adjunct scientist at the Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543. Meir Feder is with the Department of Electrical Engineering-Systems, Faculty of Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel. Alan V. Oppenheim is with the Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139. IEEE Log Number 9210882.
PY - 1993/10
Y1 - 1993/10
N2 - In a variety of contexts, observations are made of the outputs of an unknown multiple-input multiple-output linear system, from which it is of interest to identify the unknown system and to recover the input signals. This often arises, for example, with speech recorded in an acoustic environment in the presence of background noise or competing speakers, in passive sonar applications, and in data communications in the presence of cross-coupling effects between the transmission channels. In this paper we specifically consider the two-channel case in which we observe the outputs of a 2 x 2 linear time invariant system. Our approach consists of reconstructing the input signals by assuming that they are statistically uncorrelated and imposing this constraint on the signal estimates. In order to restrict the set of solutions, additional information on the true signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow’s least-squares method for noise cancellation, when the reference signal contains a component of the desired signal.
AB - In a variety of contexts, observations are made of the outputs of an unknown multiple-input multiple-output linear system, from which it is of interest to identify the unknown system and to recover the input signals. This often arises, for example, with speech recorded in an acoustic environment in the presence of background noise or competing speakers, in passive sonar applications, and in data communications in the presence of cross-coupling effects between the transmission channels. In this paper we specifically consider the two-channel case in which we observe the outputs of a 2 x 2 linear time invariant system. Our approach consists of reconstructing the input signals by assuming that they are statistically uncorrelated and imposing this constraint on the signal estimates. In order to restrict the set of solutions, additional information on the true signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow’s least-squares method for noise cancellation, when the reference signal contains a component of the desired signal.
UR - http://www.scopus.com/inward/record.url?scp=0027678421&partnerID=8YFLogxK
U2 - 10.1109/89.242486
DO - 10.1109/89.242486
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AN - SCOPUS:0027678421
SN - 1063-6676
VL - 1
SP - 405
EP - 413
JO - IEEE Transactions on Speech and Audio Processing
JF - IEEE Transactions on Speech and Audio Processing
IS - 4
ER -