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Single Channel Source Separation Using Non-Gaussian NMF and Modified Hilbert Spectrum
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Seyyed Reza Sharafinezhad1,Mohammad Eshghi2,Habib Alizadeh3
*1, Tehran University, Email : r.sharafi@ut.ac.ir
2, shahid beheshti university, Email : m-eshghi@sbu.ac.ir
3, Tarbiat Modares University, Email : Habib.alizadeh@modarec.ac.ir
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Abstract
.In this paper, a new and powerful method for Blind Source Separation (BSS) for single channel mixtures is presented. This method is based on non-Gaussian nonnegative matrix factorization (NG-NMF) in which modified Hilbert spectrum is employed. In the proposed algorithm, the Adaptive EEMD (AEEMD) is introduced to transfer the signal to the Enhancement Intrinsic Mode Functions (EIMF). The Hilbert spectrums of EIMFs are used as artificial observations.
In order to make estimated spectrum of EIMF of sources using NMF, the maximization of Non-Gaussianity is used. Then, spectra of estimated oscillation modes are transferred to the time domain by the inverse Hilbert spectrum (IHS). In order to cluster of these oscillation modes, k-means clustering algorithm based on KLD (Kullback Leibler Divergence) is used. The simulation results indicate that the proposed algorithm performs the separation of speech and interfering sounds. from a single-channel mixture, successfully.
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Keywords
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Blind Source Separation (BSS) ; Non-Gaussian Nonnegative Matrix Factorization ; Adaptive Ensemble Empirical Mode Decomposition ; modified Hilbert spectrum (HS)
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URL: http://dx.doi.org/10.7321/jscse.v5.n1.1
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