Audio Compression Using DWT and RLE Techniques

Abebe Tsegaye^1, and Girma Tariku^1,

¹Electrical and Computer Engineering Department, College of Engineering, Debre Berhan, Ethiopia

Cite this paper

Abebe Tsegaye and Girma Tariku. Audio Compression Using DWT and RLE Techniques. American Journal of Electrical and Electronic Engineering. 2019; 7(1):14-17. doi: 10.12691/AJEEE-7-1-3

Abstract

The growth of the cellular technology and wireless networks all over the world has increased the demand for digital information by manifold. This massive demand poses difficulties in handling huge amounts of data that need to be stored and transferred. To overcome this problem we can compress the information by removing the redundancies present in it. Redundancies are the major source of generating errors and noisy signals. Coding in MATLAB helps in analyzing compression of audio signals with varying bit rate and remove errors and noisy signals from the audio signals. The audio signal’s bit rate can also be reduced to remove errors and noisy signals which are suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet. This paper focuses on the audio compression process and its analysis using DWT and RLE Techniques through MATLAB by which processed audio signal can be heard with clarity and in the noiseless mode at the receiver end.

Keywords

daubechies wavelet, audio compression, Discrete Wavelet Transform (DWT), Run Length Encoding (RLE)

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	J.I. Agbinya, “Discrete Wavelet Transform Techniques in Speech Processing”, IEEE Tencon Digital Signal Processing Applications Proceedings, IEEE, New York, NY, 1996, pp 514-519.
[2]	Othman O. Khalifa, SeringHabib Harding & Aisha Hassan A. Hashim “Compression using Wavelet Transform in Signal Processing’’: An International Journal, Volume (2): Issue (5).
[3]	D. Sinha and A. Tewfik. “Low Bit Rate Transparent Audio Compression using Adapted Wavelets”, IEEE Trans. ASSP, Vol. 41, No. 12, December 1993.
[4]	Thyagaraian, K. S. “Still image and video compression with MATLAB” P.cm ISBN978-0-470-88692-2.
[5]	P. Srinivasan and L. H. Jamieson. “High Quality Audio Compression Using an Adaptive Wavelet Packet Decomposition and Psychoacoustic Modeling”, IEEE Transactions on Signal Processing, Vol 46, No. 4, April 1998.
[6]	Martin Vetterli. “Still image and video compression with Matlab”, 2nd Edition.
[7]	HarmanpreetKaur and RamanpreetKaur, “Speech compression and decompression using DCT and DWT”, International Journal Computer Technology & Applicaton, Vol 3 (4), 1501-1503 IJCTA | July-August 2012.
[8]	Mark Nelson. “Data Compression” 1st Edition, ISBN 1558514341.
[9]	C.S. Burrus, R.A Gopinath, and H.Guo. “Introduction to Wavelet Transforms’’, Prentice Hall, Englewood Cliffs, New Jersey, 1998.
[10]	I. Daubechies. Ten Lectures on Wavelelet. SIAM, Philadelphia, PA, 1992.