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The independent component analysis (ICA) is a commonly used method to find the demixing matrix for the blind source separation (BSS). For speech signals, we should solve BSS problems in the convolutive mixing model, i.e., ICA technique is extended to the frequency domain. The cross-spectral density matrices are computed for each frequency bin instead of covariance matrices in time domain. The joint...
This paper investigates several factors affecting the sensitivity of joint approximate diagonalization of a set of time varying cross-spectral matrices for blind separation of convolutive mixtures of speech signals. We study the effect of number of matrices in this set, and show that estimation of demixing system parameters is related to both several statistics of the perturbation term, occurring...
In this paper, we propose a closed loop system to improve the performance of single-channel speech separation in a speaker independent scenario. The system is composed of two interconnected blocks: a separation block and a speaker identification block. The improvement is accomplished by incorporating the speaker identities found by the speaker identification block as additional information for the...
This paper presents new results on blind separation of instantaneously mixed independent sources based on high-order statistics together with their time and frequency non-properties (i.e., the non-stationarity and non-whiteness of sources). Separation criteria of mixtures are established on a set of cumulants at different time instants using the non-stationarity of sources and/or time-delayed cumulants...
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