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Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression.
Springerplus. 2016; 5(1):2048.S

Abstract

BACKGROUND

Compressed sensing is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with the only few numbers of observations compared to conventional Shannon-Nyquist sampling, and thus reduces the storage requirements. In this study, we have proposed the 1-D discrete wavelet transform (DWT) based sensing matrices for speech signal compression. The present study investigates the performance analysis of the different DWT based sensing matrices such as: Daubechies, Coiflets, Symlets, Battle, Beylkin and Vaidyanathan wavelet families.

RESULTS

First, we have proposed the Daubechies wavelet family based sensing matrices. The experimental result indicates that the db10 wavelet based sensing matrix exhibits the better performance compared to other Daubechies wavelet based sensing matrices. Second, we have proposed the Coiflets wavelet family based sensing matrices. The result shows that the coif5 wavelet based sensing matrix exhibits the best performance. Third, we have proposed the sensing matrices based on Symlets wavelet family. The result indicates that the sym9 wavelet based sensing matrix demonstrates the less reconstruction time and the less relative error, and thus exhibits the good performance compared to other Symlets wavelet based sensing matrices. Next, we have proposed the DWT based sensing matrices using the Battle, Beylkin and the Vaidyanathan wavelet families. The Beylkin wavelet based sensing matrix demonstrates the less reconstruction time and relative error, and thus exhibits the good performance compared to the Battle and the Vaidyanathan wavelet based sensing matrices. Further, an attempt was made to find out the best-proposed DWT based sensing matrix, and the result reveals that sym9 wavelet based sensing matrix shows the better performance among all other proposed matrices. Subsequently, the study demonstrates the performance analysis of the sym9 wavelet based sensing matrix and state-of-the-art random and deterministic sensing matrices.

CONCLUSIONS

The result reveals that the proposed sym9 wavelet matrix exhibits the better performance compared to state-of-the-art sensing matrices. Finally, speech quality is evaluated using the MOS, PESQ and the information based measures. The test result confirms that the proposed sym9 wavelet based sensing matrix shows the better MOS and PESQ score indicating the good quality of speech.

Authors+Show Affiliations

Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra India.Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra India.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27995025

Citation

Parkale, Yuvraj V., and Sanjay L. Nalbalwar. "Application of 1-D Discrete Wavelet Transform Based Compressed Sensing Matrices for Speech Compression." SpringerPlus, vol. 5, no. 1, 2016, p. 2048.
Parkale YV, Nalbalwar SL. Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression. Springerplus. 2016;5(1):2048.
Parkale, Y. V., & Nalbalwar, S. L. (2016). Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression. SpringerPlus, 5(1), 2048.
Parkale YV, Nalbalwar SL. Application of 1-D Discrete Wavelet Transform Based Compressed Sensing Matrices for Speech Compression. Springerplus. 2016;5(1):2048. PubMed PMID: 27995025.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression. AU - Parkale,Yuvraj V, AU - Nalbalwar,Sanjay L, Y1 - 2016/11/30/ PY - 2016/06/25/received PY - 2016/11/25/accepted PY - 2016/12/21/entrez PY - 2016/12/21/pubmed PY - 2016/12/21/medline KW - Compressed sensing (CS) KW - Discrete wavelet transform (DWT) KW - Mean opinion score (MOS) KW - Perceptual evaluation of speech quality (PESQ) KW - Speech compression SP - 2048 EP - 2048 JF - SpringerPlus JO - Springerplus VL - 5 IS - 1 N2 - BACKGROUND: Compressed sensing is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with the only few numbers of observations compared to conventional Shannon-Nyquist sampling, and thus reduces the storage requirements. In this study, we have proposed the 1-D discrete wavelet transform (DWT) based sensing matrices for speech signal compression. The present study investigates the performance analysis of the different DWT based sensing matrices such as: Daubechies, Coiflets, Symlets, Battle, Beylkin and Vaidyanathan wavelet families. RESULTS: First, we have proposed the Daubechies wavelet family based sensing matrices. The experimental result indicates that the db10 wavelet based sensing matrix exhibits the better performance compared to other Daubechies wavelet based sensing matrices. Second, we have proposed the Coiflets wavelet family based sensing matrices. The result shows that the coif5 wavelet based sensing matrix exhibits the best performance. Third, we have proposed the sensing matrices based on Symlets wavelet family. The result indicates that the sym9 wavelet based sensing matrix demonstrates the less reconstruction time and the less relative error, and thus exhibits the good performance compared to other Symlets wavelet based sensing matrices. Next, we have proposed the DWT based sensing matrices using the Battle, Beylkin and the Vaidyanathan wavelet families. The Beylkin wavelet based sensing matrix demonstrates the less reconstruction time and relative error, and thus exhibits the good performance compared to the Battle and the Vaidyanathan wavelet based sensing matrices. Further, an attempt was made to find out the best-proposed DWT based sensing matrix, and the result reveals that sym9 wavelet based sensing matrix shows the better performance among all other proposed matrices. Subsequently, the study demonstrates the performance analysis of the sym9 wavelet based sensing matrix and state-of-the-art random and deterministic sensing matrices. CONCLUSIONS: The result reveals that the proposed sym9 wavelet matrix exhibits the better performance compared to state-of-the-art sensing matrices. Finally, speech quality is evaluated using the MOS, PESQ and the information based measures. The test result confirms that the proposed sym9 wavelet based sensing matrix shows the better MOS and PESQ score indicating the good quality of speech. SN - 2193-1801 UR - https://www.unboundmedicine.com/medline/citation/27995025/Application_of_1_D_discrete_wavelet_transform_based_compressed_sensing_matrices_for_speech_compression_ L2 - https://dx.doi.org/10.1186/s40064-016-3740-x DB - PRIME DP - Unbound Medicine ER -
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