Blind Source Separation Algorithm for Mimo Convolutive Mixtures

We consider the problem of blind source separation of MIMO convolutive mixtures for the general case where the number of sensors are greater than or equal to the number of sources. We assume that sources are non-stationary signals. The separation is performed in the frequency domain by joint minimization of the off–diagonal elements of observed signal’s cross-spectral density matrices over different epochs. We propose an efficient Newton– based algorithm over the complex Steifel manifold to minimize an appropriate cost function. We resolve the permutation problem using a novel tree structured diadic detection scheme. We find and correct wrong permutations at each frequency bin based on cross frequency correlation between diagonal elements of the output cross spectral matrices. We demonstrate the performance of the new algorithm using synthetic mixtures and real word recordings. The method has the additional advantage of fast convergence.