Convolutive blind source separation using a multiple decorrelation method

Abstract

A method and apparatus that performs blind source separation using convolutive signal decorrelation. More specifically, the method accumulates a length of input signal (mixed signal) that includes a plurality of independent signals from independent signal sources. The invention then divides the length of input signal into a plurality of T-length periods (windows) and performs a discrete Fourier transform (DFT) on the signal within each T-length period. Thereafter, estimated cross-correlation values are computed using a plurality of the averaged DFT values. A total number of K cross-correlation values are computed, where each of the K values is averaged over N of the T-length periods. Using the cross-correlation values, a gradient descent process computes the coefficients of a finite impulse response (FIR) filter that will effectively separate the source signals within the input signal. To achieve an accurate solution, the gradient descent process is constrained in that the time-domain values of the filter coefficients can attain only certain values, i.e., the time-domain filter coefficient values W(.tau.) are constrained within each T-length period to be zero for any time t>Q. In this manner, a unique solution for the FIR filter coefficients is computed and a filter produced using these coefficients will effectively separate the source signals.