CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Evaluating Performance of Compressive Sensing for Speech Signal with various Basis

by Desai Siddhi, Nakrani Naitik
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 11
Year of Publication: 2014
Authors: Desai Siddhi, Nakrani Naitik
10.5120/16388-5960

Desai Siddhi, Nakrani Naitik . Evaluating Performance of Compressive Sensing for Speech Signal with various Basis. International Journal of Computer Applications. 94, 11 ( May 2014), 23-26. DOI=10.5120/16388-5960

@article{ 10.5120/16388-5960,
author = { Desai Siddhi, Nakrani Naitik },
title = { Evaluating Performance of Compressive Sensing for Speech Signal with various Basis },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 11 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number11/16388-5960/ },
doi = { 10.5120/16388-5960 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:22.929439+05:30
%A Desai Siddhi
%A Nakrani Naitik
%T Evaluating Performance of Compressive Sensing for Speech Signal with various Basis
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 11
%P 23-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Compressive sensing is a promising focus in signal processing field, which offers a novel approach of simultaneous compression and sampling. In this technology, a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal is sparse on one basis. Encouraged by its exciting potential application in signal compression, Compressive sensing framework has been used for speech Compression. This paper shows detailed comparison of compressive sensing theory applied with different sparsity basis on 8 KHz sampled speech signal. Performance of various basis has been compared with Mean square error, Signal to noise ratio and Perceptual Evaluation of Speech Quality parameters.

References
  1. David L. Donoho. 2004. Compressive sensing. Department of statistics, Stanford University.
  2. Emmanuel J. Candes and Michael B. Wakin, "An Introduction to Compressive sampling. " IEEE signal Processing Magazine, Vol. 25, Issue. 2, 2008. 21-30.
  3. T. V. Sreenivas and W. Bastiaan klejin, "Compressive sensing for sparsely excited speech signals. " IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei,2009. 4125 – 4128.
  4. Daniele Giacobello, Mads Graesboll Christensen, Manohar N. Murthi, Soren Holdt Jenson, Marc Moonen, " Retrieving Sparse patterns using a compressed sensing framework: Applications to speech coding Based on sparse linear prediction. " IEEE Signal processing letters, vol. 17, Issue. 1, 2010. 103-106.
  5. Heung-No lee. 2011. Introduction to compressive sensing. Lecture notes. 26-29.
  6. T. S. Gunawan, O. O. Khalifa, A. A. Shafie and E. Ambikairajah, "Speech compression using compressive sensing on multicore system. " 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, 2011. 1-4.
  7. Yue Wang, Zhixing Xu, Gang Li, Liping Chang and Chuanrong Hong. "Compressive Sensing Framework for Speech Signal Synthesis Using a Hybrid Dictionary. " 4th International Congress on Image and Signal Processing (CISP), Vol. 5, Shanghai, 2011. 2400 – 2403.
  8. Ahmed Sabir. 2011. Compressive sensing for speech signals in mobile system. M. S. Thesis. The University of Texas.
  9. Liban A. kassim and T. S. Gunawan, "Evaluation of sparsifying algorithm for speech signals. " International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, 2012. 308 – 313.
  10. Vinayak Abrol, Pulkit Sharma and Sumit Budhiraja,"Evaluating performance of compressed sensing for speech signal. " IEEE 3rd International advance computing conference (IACC), Ghaziabad, 2013. 1159-1164.
  11. The PESQ Algorithm as the Solution for Speech Quality Evaluation on 2. 5G and 3G Network Technical Paper. URL: http://www. cn. ascom. com/cn/pesq-3g. pdf
  12. NOIZEUS: A noisy speech corpus for evaluation of speech enhancement algorithms. URL: http://ecs. utdallas. edu/loizou/speech/noizeus
  13. Compressive Sensing in speech processing: A survey based on sparsity and sensing matrix". IJETAE vol. 3 issue 12. December- 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Sensing Matrix Sparsity Basis Reconstruction Algorithm

Powered by PhDFocusTM