Laplacian mixture modeling for overcomplete mixing matrix in wavelet packet domain by adaptive EM-type algorithm and comparisons

Authors:
Behzad Mozaffary;Mohammad A. Tinati
Affiliations:
Faculty of Electrical and Computer Engineering, Univercity of Tabriz, Tabriz, East Azerbaijan, Iran;Faculty of Electrical and Computer Engineering, Univercity of Tabriz, Tabriz, East Azerbaijan, Iran
Venue:
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Year:
2006

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Abstract

Speech process has benefited a great deal from the wavelet transforms. Wavelet packets decompose signals in to broader components using linear spectral bisecting. In this paper, mixtures of speech signals are decomposed using wavelet packets, the phase difference between the two mixtures are investigated in wavelet domain. In our method Laplacian Mixture Model (LMM) is defined. An Expectation Maximization (EM) algorithm is used for training of the model and calculation of model parameters which is the mixture matrix. And then we compare estimation of mixing matrix by LMM-EM with different wavelet. Therefore individual speech components of speech mixtures are separated.