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Reseach Article

Speech Compression for Better Audibility using Wavelet Transformation with Adaptive Kalman Filtering

by T. Siva Nagu, K. Jyothi, V. Sailaja
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 11
Year of Publication: 2012
Authors: T. Siva Nagu, K. Jyothi, V. Sailaja
10.5120/8462-2209

T. Siva Nagu, K. Jyothi, V. Sailaja . Speech Compression for Better Audibility using Wavelet Transformation with Adaptive Kalman Filtering. International Journal of Computer Applications. 53, 11 ( September 2012), 1-5. DOI=10.5120/8462-2209

@article{ 10.5120/8462-2209,
author = { T. Siva Nagu, K. Jyothi, V. Sailaja },
title = { Speech Compression for Better Audibility using Wavelet Transformation with Adaptive Kalman Filtering },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 11 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number11/8462-2209/ },
doi = { 10.5120/8462-2209 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:49.652790+05:30
%A T. Siva Nagu
%A K. Jyothi
%A V. Sailaja
%T Speech Compression for Better Audibility using Wavelet Transformation with Adaptive Kalman Filtering
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 11
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In mobile communication systems, service providers are continuously met with the challenge of accommodating more users within a limited allocated bandwidth. For this reason, manufactures and service providers are continuously in search of low bit-rate speech coders that deliver toll-quality speech. This paper deals with speech compression based on discrete wavelet transforms and Adaptive Kalman filter. We used English words for this experiment. We could successfully compressed and reconstructed the words with perfect audibility by using above technique. Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The wavelet transform of a signal decomposes the original signal into wavelets coefficients at different scales and positions. These coefficients represent the signal in the wavelet domain and all data operations can be performed using the corresponding wavelet coefficients. In this paper Code was simulated using MATLAB. The result obtained from Wavelet Coding was compared with Adaptive Kalman with Wavelet Coding. From the results we noticed that the performance of Wavelet Coding with Adaptive Kalman Filter is better than wavelet transform.

References
  1. J. N. Holmes, Speech Synthesis and Recognition, Chapman & Hall, London, 1988.
  2. A. Gersho, "Speech Coding," Digital Speech Processing, A. N. Ince, ed. , Kluwer Academic Publishers, Boston, 1992, pp. 73-100.
  3. Hatem Elaydi, Mustafa I. Jaber, Mohammed B. Tanboura, "Speech Compression using Wavelets" Electrical & Computer Engineering Department Islamic University of Gaza, Palestine,2010.
  4. V. Viswanathan, W. Anderson, J. Rowlands, M. Ali and A. Tewfik, "Real-Time Implementation of a Wavelet-Based Audio Coder on the T1 TMS320C31 DSP Chip," 5th International Conference on Signal Processing Applications & Technology (ICSPAT), Dallas, TX, Oct. 1994.
  5. E. B. Fgee, W. J. Phillips, W. Robertson, "Comparing Audio Compression using Wavelets with other Audio Compression Schemes," IEEE Canadian Conference on Electrical and Computer Engineering, IEEE, Edmonton, Canada, 1999, pp. 698-701.
  6. W. Kinsner and A. Langi, "Speech and Image Signal Compression with Wavelets," IEEE Wescanex Conference Proceedings, IEEE, New York, NY, 1993, pp. 368-375.
  7. C. J. Li, "Non-Gaussian, non-stationary, and nonlinear signal processing methods – with applications to speech processing and channel estimation," Ph. D. dissertation, Aarlborg University, Denmark, Feb. 2006.
  8. P. Loizou, Speech Enhancement: Theory and Practice, 1st ed. CRC Press LLC, 2007.
  9. Stephen So, Kamil K. W´ojcicki, Kuldip K. Paliwal "Single-channel speech enhancement using Kalman filtering in the modulation domain" Sept. 2010, Signal Processing Laboratory, Griffith School of Engineering, Griffith University, Brisbane, QLD, Australia, 4111
Index Terms

Computer Science
Information Sciences

Keywords

Wavelet Transform coding (DWT) Adaptive Kalman filtering