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Speech Signal Reconstruction using Two-Step Iterative Shrinkage Thresholding Algorithm

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Rachit Saluja, Susmita Deb
10.5120/ijca2016912212

Rachit Saluja and Susmita Deb. Speech Signal Reconstruction using Two-Step Iterative Shrinkage Thresholding Algorithm. International Journal of Computer Applications 153(11):1-4, November 2016. BibTeX

@article{10.5120/ijca2016912212,
	author = {Rachit Saluja and Susmita Deb},
	title = {Speech Signal Reconstruction using Two-Step Iterative Shrinkage Thresholding Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2016},
	volume = {153},
	number = {11},
	month = {Nov},
	year = {2016},
	issn = {0975-8887},
	pages = {1-4},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume153/number11/26444-2016912212},
	doi = {10.5120/ijca2016912212},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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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Keywords

Compressive Sensing, Convex Optimization, Two-Step Iterative Shrinkage/Thresholding Algorithms, l1 Magic, Gradient Projection for Sparse Reconstruction

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