In this paper, we propose two versions of a correlation-based blind source separation (BSS) method. Whereas its basic version operates in the time domain, its extended form is based on the timefrequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of the (permuted scaled) mixing matrix in T...

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or ‘sources’ from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but...

In this paper, we describe a new blind source separation (BSS) method that uses spatial information derived from the direction of arrival (DOA) estimates of each direct and reflected sound. The method we proposed has the following steps: (1) each DOA is estimated using matching pursuit and re-optimized after each new DOA is estimated, (2) using these DOA estimates, the mixing matrix is also est...

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Blind source separation (BSS) for convolutive mixtures can be performed efficiently in the frequency domain, where independent component analysis (ICA) is applied separately in each frequency bin. However, frequencydomain BSS involves two major problems that must be solved. The first is the permutation problem: the permutation ambiguity of ICA should be aligned so that a separated signal in the...

In Different Fields, The Problem Of Blind Source Separation (BSS) Is Known As A Collection Of Linear Combinations Of Unknown Sources And Worse The Coefficients Of The Linear Combinations That Are Unknown. The Main Problem Is To Estimate The Matrix Of The Combination Coefficients (Mixing Matrix) And To Reconstruct The Sources According To It. The Quality Of Separation Of Sources From Mixtures Dr...