An Analysis on Wavelet Applications as Speech Data Mining Tools
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Senthil Devi K A, Dr. Srinivasan B|
|DOI : 10.14445/22312803/IJCTT-V43P125|
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):160-165, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
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.
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Wavelet theory, wavelet transformation, wavelet packet decomposition, multiwavelet, multiwavelet packet, data mining and speech processing.