An Analysis on Wavelet Applications as Speech Data Mining Tools

摘要

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 nonstationary characters. It has been shown that wavelets can approximate time varying nonstationary 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.