论文信息 - Signal and image source separations by wavelet analysis

Signal and image source separations by wavelet analysis

Blind Source Separation (BSS) is one of the hottest emerging areas in signal processing. The purpose of BSS is to separate and to estimate the original sources from the sensor array, without knowing the transmission channel characteristics. Besides methods based on independent component analysis which is one of the most powerful tools for BSS, several methods based on time-frequency analysis have been proposed. One of them is the quotient signal estimation method which can estimate the unknown number of sources. We present a new method using wavelet analysis and apply it to signal and image source separations.

[1] E. Oja,et al. Independent Component Analysis , 2001 .

[2] A. Morimoto,et al. Blind source separation of spatio-temporal mixed signals using time-frequency analysis , 2009 .

[3] Simon Haykin,et al. The Cocktail Party Problem , 2005, Neural Computation.

[4] Michael Felsberg,et al. The monogenic signal , 2001, IEEE Trans. Signal Process..

[5] Scott Rickard,et al. Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.

[6] Image separation using the monogenic wavelet transform , 2010, 2010 10th International Symposium on Communications and Information Technologies.

[7] E. C. Cherry. Some Experiments on the Recognition of Speech, with One and with Two Ears , 1953 .

[8] Andrzej Cichocki,et al. Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[9] Özgür Yilmaz,et al. Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[10] Radu Balan,et al. Statistical properties of STFT ratios for two channel systems and applications to blind source separation , 2000 .

[11] Mathematical background for a method on quotient signal decomposition , 2007 .

[12] P. Krishnaprasad,et al. Quotient Signal Estimation , 2005 .