CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Novel Approach to Synthesize Sounds of Some Indian Musical Instruments using DWT

by Raghavendra Sharma, V Prem Pyara
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 13
Year of Publication: 2012
Authors: Raghavendra Sharma, V Prem Pyara
10.5120/6840-9375

Raghavendra Sharma, V Prem Pyara . A Novel Approach to Synthesize Sounds of Some Indian Musical Instruments using DWT. International Journal of Computer Applications. 45, 13 ( May 2012), 19-22. DOI=10.5120/6840-9375

@article{ 10.5120/6840-9375,
author = { Raghavendra Sharma, V Prem Pyara },
title = { A Novel Approach to Synthesize Sounds of Some Indian Musical Instruments using DWT },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 13 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number13/6840-9375/ },
doi = { 10.5120/6840-9375 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:31.414125+05:30
%A Raghavendra Sharma
%A V Prem Pyara
%T A Novel Approach to Synthesize Sounds of Some Indian Musical Instruments using DWT
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 13
%P 19-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a novel algorithm based on Discrete Wavelet Transform (DWT) approach has been applied to synthesize the sounds produced by a few traditional Indian musical instruments, viz. flute, shehnai and sitar. In this algorithm, the level of decomposition of wavelets is varied till the error norm between the original signal and that generated through DWT is below a desired level. It is observed that when the wavelet decomposition level is varied, the energy retained in wavelet coefficients varies with the type of the wavelet and its decomposition level. It is further observed that the maximum level of decomposition for the three sound signals is different and the signals are also reconstructed with the wavelet coefficients only up-to the maximum level of decomposition with lesser number of samples. The quality of sound as obtained through this algorithm is perceptually close to original sound signal.

References
  1. Mallat, S. G. 2009. A Wavelet Tour of Signal Processing. The Sparse Way, 3rd Edition, Academic Press.
  2. Tobias, J. S. 1970. Foundations of Modern Auditory Theory, Vol. 1, Academic Press, New York.
  3. Bultheel, A. 2003. Wavelets with applications in signal and Image processing. Text pages 1-181.
  4. Panos E. Kudumakis and Mark B. Sandler. 1993. Synthesis Of Audio Signals using the Wavelet Transform. IEEE Colloquium on Audio DSP – Circuits and Systems (Digest No. 1993/219), pp. 1- 4, vol. 4.
  5. Vetterli, M. , and Kovacevic, J. 2007. Wavelets and Sub band Coding. Prentice Hall PTR, Englewood Cliffs, New Jersey.
  6. Ramchandran, K. , Vetterli, M. and Herley, C. 1996. Wavelets, Sub band Coding and Best Bases, Proceedings of IEEE, Vol. 84, No. 4, pp. 541-560.
  7. Vetterli, M. , and Kovacevic, J. 1995. Wavelet and Sub band Coding. Prentice Hall.
  8. Vetterli, M. and Harley, C. 1992. Wavelets and Filter Banks: Theory and Design, IEEE Transaction on Signal Processing. Vol. 40, No. 9.
  9. Galand, C. R. and Nussbaumber, H. J. 1984. New Quadrature Mirror Filter Structures, IEEE Transaction on Acoustics Speech and Signal Processing, Vol. 32, No. 3, pp. 531-552.
  10. Kumar, A. , Singh, G. K, and Anand, R. S. 2008. Near Perfect Reconstruction Quadrature Mirror Filter, World Academy of Science, Engineering and Technology, Vol. 37, pp. 204-207.
  11. K. P Soman, K. I Ramchandran, N. G Resmi, 2010. Insight into Wavelets from theory to practice, PHI Learning Private Limited New Delhi.
  12. Szu, H. , Sheng, Y. and Chen, J. 1992. Wavelet Transform as a Bank of Matched Filters, Appl. Optic Vol. 31, pp. 3267-3277.
  13. Mallat, S. G. 1989. A Theory for Multi-resolution Signal Decomposition: The Wavelet Representation, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 11, pp. 674 – 693.
  14. Burrus, C. S. , Gopinath, A. R, Wickerhauser, M. V. 1996. Wavelets and Time frequency Analysis, Proceedings of IEEE, Vol. 84, No. 4, pp. 523-540.
Index Terms

Computer Science
Information Sciences

Keywords

Approximate Coefficients Detail Coefficients. Filter Bank Energy In Coefficients

Powered by PhDFocusTM