Sparsity-based single-channel blind separation of superimposed AR processes

Ron Shiff*, Arie Yeredor

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


We address the blind separation of two autoregressive (AR) processes from a single mixture thereof, when their respective driving-noise ("innovation") sequences are known to be temporally sparse. Unlike other single-channel separation schemes, which use dictionary- learning, our method essentially estimates the sparsifying transformation of each source directly from the ob- served mixture (by estimating the respective AR parameters), and therefore does not require a training stage. We cast the problem as a constrained, non-convex ℓ 1- norm minimization and propose an iterative solution scheme, which iterates between linear-programming- based estimation of the respective driving-sequences given estimates of the AR parameters, and gradient- based refinement of the estimated AR parameters given the estimated driving sequences. Near-perfect separation is demonstrated using a simulated example.

Original languageEnglish
Pages (from-to)1479-1483
Number of pages5
JournalEuropean Signal Processing Conference
StatePublished - 2010
Event18th European Signal Processing Conference, EUSIPCO 2010 - Aalborg, Denmark
Duration: 23 Aug 201027 Aug 2010


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