Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources Wenwu Wang Saeid Sanei Jonathon Chambers 2134/5775 https://repository.lboro.ac.uk/articles/journal_contribution/Penalty_function-based_joint_diagonalization_approach_for_convolutive_blind_separation_of_nonstationary_sources/9569048 A new approach for convolutive blind source separation (BSS) by explicitly exploiting the second-order nonstationarity of signals and operating in the frequency domain is proposed. The algorithm accommodates a penalty function within the cross-power spectrum-based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization. This leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient-based descent algorithm. Using this approach, not only can the degenerate solution induced by a unmixing matrix and the effect of large errors within the elements of covariance matrices at low-frequency bins be automatically removed, but in addition, a unifying view to joint diagonalization with unitary or nonunitary constraint is provided. Numerical experiments are presented to verify the performance of the new method, which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals, in particular, in terms of shape preservation and amplitude ambiguity reduction, as compared with the conventional second-order based algorithms for convolutive mixtures that exploit signal nonstationarity. 2010-01-11 12:38:43 Blind source separation Convolutive mixtures Frequency domain Orthogonal/nonorthogonal constraints Penalty function Speech signals Mechanical Engineering not elsewhere classified