Joint Diagonalization (JD) is a well-known method for blind signal separation (BSS) by exploiting the nonstationarity of signals. In this paper, we propose Synchronized Joint Diagonalization (SJD) that solves multiple JD problems simultaneously and tries to synchronize the activity of the same signal along the time axis over the multiple JD problems. SJD attains not only signal separation by the mechanism of JD but also permutation alignment by the synchronization when applied to frequency-domain BSS. Although the formulation of SJD starts from the minimization of multi-channel Itakura-Saito divergences between a covariance matrix and a diagonal matrix, the simplified cost function with the finest time blocks becomes similar to that of Independent Vector/Component Analysis (IVA/ICA). We discuss the relationship among SJD and existing techniques. Experimental results on speech separation are shown to demonstrate the behavior of these methods.
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