An Analysis on Wavelet Applications as Speech Data Mining Tools

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-43 Number-3
Year of Publication : 2017
Authors : Senthil Devi K A, Dr. Srinivasan B
DOI :  10.14445/22312803/IJCTT-V43P124


Senthil Devi K A, Dr. Srinivasan B  "An Analysis on Wavelet Applications as Speech Data Mining Tools". International Journal of Computer Trends and Technology (IJCTT) V43(3):156-159, January 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Recently there has been significant development in the use of wavelet methods in various data mining and signal processing applications. Fourier Transform methods are not well suited for detection and classification of speech signals which possess non-stationary characters. It has been shown that wavelets can approximate time varying non-stationary signals in a better way than the Fourier transform representing the signal on both time and frequency domains. Furthermore, wavelet decomposition allows analyzing a signal at different resolution levels. This paper presents general overview of wavelets and their applications in speech as data mining tools. It first presents a data mining framework in which the overall process is divided into smaller components. It discusses the impact of wavelets on speech and data mining research.

1. Tao Li, Sheng Ma, Mitsunori Ogihara, Wavelet methods in data mining, Chapter 27, Data Mining and Knowledge Discovery Handbook , Springer, 2005, pp 603-626.
2. Olivier Rioul and Martin Vetterly, Wavelet and Signal Processing, IEEE Signal processing magazine, October 1991.
3. M. H. Farouk, Applications of Wavelets in Speech Processing, Springer Briefs in Electrical and Computer Technology, 2013.
4. Cristina Laura Stolojescu, A Wavelets Based Approach for Time Series Mining, Ph.D thesis, Politehnica University of Timisoara, 2011.
5. Michel Misiti, Yves Misiti, Georges Oppenheim, Jean-Michel Poggi, Wavelet Toolbox Computing Visualization Programming User’s Guide, version 1, 1996.
6. Salhi L, Talbi M, and Cherif A, Voice Disorders Identification using Hybrid Approach: Wavelet Analysis and Multilayer Neural Networks, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:2, No:9, 2008
7. George Tzanetakis, Georg Essl, Perry Cook, Audio Analysis using the Discrete Wavelet Transform Organized sound, Vol. 4(3), 2000.
8. Vimal Krishnan V.R, Babu Anto P, Features of Wavelet Packet Decomposition and Discrete Wavelet Transform for Malayalam Speech Recognition, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
9. Jian Ping Li, Stephane Jaffard, C Y Suen, John Daugman, Victor Wickerhauser, Bruno Torresani, John Yen, Ning Zhong, Sankar K Pal, Wavelet Analysis amd Active Media Technology, Proceedings of the International Computer Congress 2004, Logistical Engineering University, P R China, 28 – 30 May 2004.
10. S.S. Tamboli1, Dr. V. R. Udupi., Image Compression using Haar Wavelet Transform, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 8, 2013.
11. Kannan K., Arumuga Permal, Arumuga Perumals, Arulmozhih , Area level fusion of Multi-focused Images using Multi-Stationary Wavelet Packet Transform, International Journal of Computer Applications (0975 – 8887) Volume 2 – No.1, May 2010.
12. Stephane Mallat, A Wavelet Tour of Signal Processing a sparse way, book published by Academic Press, October, 2008.
13. Farooq O, Datla, Wavelet – based denoising for robust feature extraction of speech recognition, Electron, lett. 39(1), 163-165, 2003.
14. Senthil devi K.A., Dr.Srinivasan B, A novel Keyword Spotting Algorithm in speech mining using wavelet”, International Journal of Current Research Vol. 8, Issue, 08, pp.36943-36946, August, 2016.
15. B.T. Tan, M. Fu, A. Spray, P. Dermody, The use of wavelet transform for phoneme recognition, Proceedings of the 4th International Conference of Spoken Language Processing Philadelphia, Vol. 4, USA, October 1996, pp.2431-2434.
16. Bartosz Siolko, Suresh Manandhar, Richard C.Wilson and Mariusz Ziolko, Wavelet Method of Speech Segmentation, IEEE Xplore, Proceeding in Signal Processing Conference, 2006.

Wavelet theory, wavelet transformation, wavelet packet decomposition, multiwavelet, multiwavelet packet, data mining and speech processing.