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.
|Number of pages||5|
|Journal||European Signal Processing Conference|
|State||Published - 2010|
|Event||18th European Signal Processing Conference, EUSIPCO 2010 - Aalborg, Denmark|
Duration: 23 Aug 2010 → 27 Aug 2010