We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Audio Compression using Multiple Transformation Techniques

by Rafeeq Mohammad, M. Vijaya Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 13
Year of Publication: 2014
Authors: Rafeeq Mohammad, M. Vijaya Kumar
10.5120/15043-3405

Rafeeq Mohammad, M. Vijaya Kumar . Audio Compression using Multiple Transformation Techniques. International Journal of Computer Applications. 86, 13 ( January 2014), 9-14. DOI=10.5120/15043-3405

@article{ 10.5120/15043-3405,
author = { Rafeeq Mohammad, M. Vijaya Kumar },
title = { Audio Compression using Multiple Transformation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 13 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number13/15043-3405/ },
doi = { 10.5120/15043-3405 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:30.430627+05:30
%A Rafeeq Mohammad
%A M. Vijaya Kumar
%T Audio Compression using Multiple Transformation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 13
%P 9-14
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper presents a comparative study of audio compression using multiple transformation techniques. Audio compression with different transform techniques like Discrete Cosine Transform, Wavelet Transform, Wavelet Packet Transform (W. P. T) & Cosine Packet Transform is analyzed and compression ratio for each of the transformation techniques is obtained. Mean Compression ratio is calculated for all of the techniques and compared. Performance measures like signal to noise ratio (SNR), normalized root mean square error (NRMSE), retained signal energy (RSE) are also calculated and compared for each transform technique. Transform based compressed signals are encoded with encoding techniques like Run-length Encoding (R. L. E) and Mu-Law Encoding to reduce the redundancies. From the comparison it is clear that Discrete wavelet transform gives better compression ratio of about 27. 8593 compared with the other three transforms. Mean SNR value is minimum for DCT 29. 2830, and comparatively higher mean SNR value 43. 4037 for CPT.

References
  1. G. Rajesh, A. Kumar, K. Ranjeet, "Speech Compression using Different Transformation Techniques". IEEE International Conference on Computer and Communication Technology, pp: 146 -151, 2011.
  2. SmitaVasta, O. P. Sahu, "Speech Compression Using Discrete Wavelet Transform and Discrete Cosine transform". International Journal of Engineering Research and Technology" Vol. 1 (5) July-2012.
  3. Chakresh Kumar, Chandra Shekhar, Ashu Soni, Bindu Thakral "Implementation of Audio signal by using wavelet transform. '' International Journal of Engineering Science and Technology. Vol. 2(10), 2010.
  4. Pramila Srinivasan, Leah H. Jamieson, "High quality audio compression using an adaptive wavelet packet decomposition and psychoacoustic modelling. " IEEE-transactions on Signal processing. pp: 100- 108, 1999.
  5. M. Siffuzzaman, M. R. Islam, and M. Z. Ali, "Applications of Wavelet Transform and its Advantages compared to Fourier Transform, by Journal of Physical Sciences, Vol. 13, pp:121-134, October-2009.
  6. Jakub Galka, Mariusz Ziolko, "Best basis selection of the Wavelet Packet compression techniques Cosine Transform in speech analysis". AGH university of science & technology, unpublished.
  7. Mark A. Castellano, Todd Hiers, Rebecca Ma, "Mu-Law & A-law companding with McBSP or Software. " Application Report, Texas Instruments, April-2000.
  8. P. Fischer, "Multi resolution analysis for 2D turbulence", Wavelets – Cosine packets comparative study. Discrete and continuous dynamical systems – Series B Volume 5, August 2005.
  9. Xianjie Zha, Rongshan Fu, Zhiyang Dai , Bin Liu, " Noise reduction in interferograms using the wavelet packet transform and wiener filtering. IEEE Geoscience and remote sensing letters, Vol. 5, No. 3, July 2008.
  10. Benito Carneo, Andrzej Drygajlo, "Perceptual speech coding and enhancement using Frame –synchronized fast wavelet packet transform algorithms". IEEE transactions on signal processing, Vol. 47, No. 6, June 1999.
  11. Nandini Basumallick, Narasimhan S. V "A discrete cosine adaptive harmonic wavelet packet and its application to signal compression" Journal of Signal and Information Processing, Scientific Research JSIP pp:63-76, November 2010.
  12. Nicolas Ruiz Reyes, Man,uel Rosa Zurera, Francisco Lopez Ferreras, Pilar Jarabo Amores. "Adaptive wavelet packet analysis for audio coding purposes. " Elsevier Signal processing 83, pp: 919-929, 2003.
  13. P. Prakasam and M. Madheswaram "Adaptive algorithm for speech compression using cosine packet transform. IEEE International Conference on Intelligent and Advanced Systems, pp. 1168-1172, 2007.
  14. K. P. Soman, K. I. Ramachandran, "Insight to Wavelets" second edition 2005, by Prentice Hall of India. ISBN-81-203-2902-3
  15. Raghuveer M. Rao, Ajit S. Bopadikar, "Wavelet Transforms – Introduction to Theory and Applications". Pearson Education Asia. 1998 Pearson education, Inc. ISBN:81-7808-251-9
  16. Lan Mc Loughlin "Applied Speech and Audio Processing" Cambridge University Press, South Asian Edition .
  17. . Dorina Isar, Alexandru Isar, "Speech adaptive compression using cosine packets", Academia Tehnica Militara , Bucuresti, Communications 2002
  18. Critobal Rivero , Prabhat Mishra "Lossless audio compression-A case study" CISE technical report 08-415, August 2008
  19. "A-Law and Mu-law companding", Revision 1. 0, Young Engineering Data sheet. www. young-engineering. com
  20. H. G Stark "Wavelets and signal processing", Springer International Edition, Springer-VerlagBerlin Heidelberg.
  21. Mat Hans and Ronald W. Schafer , "Lossless Compresssion of Digita Audio" Hewlett Packard Data Sheet, HPL-1999-144, November-1999.
Index Terms

Computer Science
Information Sciences

Keywords

CPT WPT NRMSE RSE RLE