Speech Signal Reconstruction using Two-Step Iterative Shrinkage Thresholding Algorithm
نویسندگان
چکیده
The idea behind Compressive Sensing(CS) is the reconstruction of sparse signals from very few samples, by means of solving a convex optimization problem. In this paper we propose a compressive sensing framework using the Two-Step Iterative Shrinkage/ Thresholding Algorithms(TwIST) for reconstructing speech signals. Further, we compare this framework with two other convex optimization algorithms, l1 Magic and Gradient Projection for Sparse Reconstruction(GPSR). The performance of our framework is demonstrated via simulations and exhibits a faster convergence rate and better peak signal-to-noise ratio(PSNR).
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