Analysis of Different Wavelets by Correlation
Ritesh Jain1, Suraiya Parveen2
1Ritesh Jain, Lecturer, GND Polytechinc, Rohini, GNCTD, India.
2Suraiya Parveen, Computer Science, Hamdard University, New Delhi, India.
Manuscript received on March 25, 2013. | Revised Manuscript received on April 10, 2013. | Manuscript published on April 30, 2013. | PP: 469-471 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1476042413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world, speech is an integral part of digital communication. The removal of noise in analog and digital communication has been a daunting task for many years. Noise is an unwanted signal that hinders communication. There are various methods to help restore a speech from noisy distortions. Wavelets have by now established themselves to be an invaluable accumulation to the analyst’s compilation of tools and go on to enjoy a rapidly increasing recognition in their brief account of the signal processing field. Wavelet analysis is competent of enlightening aspects of data that other signal study techniques miss. In addition, it affords a diverse view of data than those obtainable by conventional techniques. Wavelet analysis can often compress or de-noise a signal without appreciable degradation. Study in the field of Wavelets has shown that Wavelet decomposition is a capable method as other methods of denoising. In this paper, the author compares the performances of Daubechies, Coiflet and Symlet Wavelets for different values of their order for an audio signal. Further, the variation of threshold values with correlation has been investigated.
Keywords: Noise, Daubechies.