BLIND SOURCE SEPARATION ALGORITHM FOR CONVOLUTIVE SPEECH MIXTURES USING JOINT BLOCK-DIAGONALIZATION
XU Shun, CHEN Shao-rong, LIU Yu-lin(Chongqing Communication Institute, DSP Lab, Chongqing 400035, China)
The method proposed resets the sampling mixture signals and transforms the convolutive BSS into instantaneous BSS. The joint approximate diagonalization algorithm is generalized, making use of the non-stationarity and short-time stationarity of speech sources, and the joint difference correlation matrix and joint block-diagonalization cost function are defined. Through robust whitening process and resolving optimization problem, the covolutive blind speech source separaton is realized. The algorithm avoids the convolution calculation and domain transformation to decrease the complexity. Computer simulation shows the effectiveness, while the further performance analysis is done by comparing the linear-pridiction-based convolutive BSS algorithm with the natural gradient convolutive BSS algorithm. The effect of data length parameter on the signal to interference ratio(SIR) is also discussed.