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


Vol. 37,  No. 12, pp. 1122-1132, Dec.  2012


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  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.

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  Cite this article

[IEEE Style]

S. Jeong and D. Lim, "A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 12, pp. 1122-1132, 2012. DOI: .

[ACM Style]

Seongmoon Jeong and Dongmin Lim. 2012. A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing. The Journal of Korean Institute of Communications and Information Sciences, 37, 12, (2012), 1122-1132. DOI: .

[KICS Style]

Seongmoon Jeong and Dongmin Lim, "A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 12, pp. 1122-1132, 12. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880