Convolution Blind Separation Of Broadband Non-white Signals

Blind source separation (BSS) is an emerging technique of array processing during the last twenty years. The aim of BSS is to recover the statistical independent source signals only from the observational signals without any knowledge of source signals and mixed channel. The process is also referred to as the independent component analysis (ICA). Since it needs no knowledge of source signals and mixture model, the technology of BSS has been widely applied in many fields, such as speech enhancement with multi-microphone, antenna array processing for wireless communication, separation for multiple mobile signals, direction of arrival (DOA) estimation in sensor array, multi-path channels identification and equalization and so on.In this paper, a convolutive blind source separation (BSS) algorithm based on a double-iteration method is proposed to process the convolutive mixed non-white broadband signals. By sliding Fourier transform (SFT), the convolutive mixture problem is changed into instantaneous case in time-frequent domain, which can be solved by applying an instantaneous separation method for every frequent bin. A novel cost function for each frequent bin based on joint diagonalization of a set of correlation matrices with multiple time-lags is constructed. Through combination of the proposed double-iteration method with a restriction on the length of inverse filter in time domain, the inverse of transfer channel or separation matrix, which has consistent permutations for all frequencies, can be estimated. Then it is easy to calculate the recovered source signals.The results of simulations also illustrate the algorithm has not only fast convergence performance, but also higher recovered accuracy and output SER (Signal to Error of reconstruction Ratio).