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http://dx.doi.org/10.7840/kics.2012.37A.12.1122

A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing  

Jeong, Seongmoon (경상대학교 전자공학과)
Lim, Dongmin (경상대학교 전자공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.37A, no.12, 2012 , pp. 1122-1132 More about this Journal
Abstract
Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.
Keywords
adaptive compressed sensing; dictionary learning; frame based speech signal processing;
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  • Reference
1 R. G. Baraniuk, "Compressive sensing," IEEE Signal Proc. Mag., vol. 24, no. 4, pp. 118-121, Jul. 2007.
2 E. J. Candes and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Proc. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.   DOI   ScienceOn
3 T. V. Sreenivas and W. B. Kleijn, "Compressive sensing for sparsely excited speech signals," in Proc. ICASSP, pp. 4125-4128, Apr. 2009.
4 T. Xu, Z. Yang and X. Shao, "Adaptive compressed sensing of speech signal based on data-driven dictionary," in Proc. APCC 2009, pp. 257-260, Sep. 2009.
5 T. S. Gunawan, O. O. Khalifa, A. A. Shafie and E. Ambikairajah, "Speech compression using compressive sensing on a multicore system," in Proc. ICOM 2011, May 2011.
6 M. G. Jafari and M. D. Plumbley, "Speech denoising based on a greedy adaptive dictionary algorithm," in Proc. European Signal Proc. Conf., pp. 1423-1426, Aug. 2009.
7 J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inform. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.   DOI   ScienceOn
8 S. S. Chen, D. L. Donoho and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Review, vol. 43, no. 1, pp. 129-159, 2001.   DOI   ScienceOn
9 M. Ahron, M. Elad and A. Bruckstein, "K-SVD: an algorithm for designing overcomplete dictionaries for sparse representations," IEEE Trans. on Signal Proc., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.   DOI   ScienceOn
10 K-SVD and OMP Matlab toolbox, from http://www.cs.technion.ac.il/-elad/software/.
11 ITU-T Study Group 12, "Perceptual evaluation of speech quality(PESQ): an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs," ITU-T Recommendation pp. 862, Feb. 2001.
12 M. G. Jafari and M. D. Plumbley, "Fast dictionary learning for sparse representations of speech signals," IEEE signal proc., vol. 5, no. 5, pp. 1027-1030, Sep. 2011.
13 S. Kunis and H. Rauhut, "Random sampling of sparse trigonometric polynomials, II. Orthogonal matching pursuit versus basis pursuit," Foundations of Computational Mathematics, vol. 8, no. 6, pp. 737-763, Dec. 2008.   DOI