Special issue: New theories and technological advancesNo Access

Multistage blind source separations by wavelet analysis

Ryuichi Ashino

Mathematical Sciences, Osaka Kyoiku University, Kashiwara, Osaka 582-8582, Japan

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Takeshi Mandai

Department of Engineering Science, Osaka Electro-Communication University, Neyagawa, Osaka 572-8530, Japan

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, and

Akira Morimoto

Information Science, Osaka Kyoiku University, Kashiwara, Osaka 582-8582, Japan

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https://doi.org/10.1142/S0219691314600042Cited by:5 (Source: Crossref)

Abstract

The purpose of blind source separation is to separate the original sources from the sensor array, without knowing the transmission channel characteristics. Besides methods based on independent component analysis, several methods based on time-frequency analysis have been proposed. In this paper, a new method of multistage separation is proposed, which improves our formerly proposed methods using the time-scale information matrix based on the continuous multiwavelet transform.

Keywords:

AMSC: 42C40, 94A08, 65T60

References

R. Ashinoet al., Information 10(5), 555 (2007). Web of Science, Google Scholar
R. Ashinoet al., Appl. Anal. 91(4), 617 (2012). Crossref, Web of Science, Google Scholar
R. Ashinoet al., Appl. Anal. 88(3), 425 (2009). Crossref, Web of Science, Google Scholar
R. Balan and J. Rosca, Statistical properties of STFT ratios for two channel systems and applications to blind source separation, Proc. 2nd Int. Workshop on Independent Component Analysis and Blind Signal Separation (2000) pp. 19–22. Google Scholar
C. Cherry, J. Acoust. Soc. Am. 25(5), 975 (1953). Crossref, Web of Science, Google Scholar
A. Cichocki and S. Amari , Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications ( John Wiley , Chichester, West Sussex, England , 2002 ) . Crossref, Google Scholar
K. Gröchenig , Foundations of Time-Frequency Analysis ( Birkhäuser , Boston, MA , 2001 ) . Crossref, Google Scholar
S. Haykin and Z. Chen, Neural Comput. 17(9), 1875 (2005). Crossref, Web of Science, Google Scholar
A. Jourjine, S. Rickard and O. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing N sources from 2 mixtures, Proc. 2000 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing5 (2000) pp. 2985–2988. Google Scholar
T. Kohonen , Self-Organizing Maps , 2nd edn. ( Springer-Verlag , Berlin , 1997 ) . Crossref, Google Scholar
A. Morimoto, R. Ashino and T. Mandai, Image separation using the monogenic wavelet transform, Proc. 10th Int. Symp. on Communications and Information Technologies (ISCIT 2010) (2010) pp. 707–712. Google Scholar