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The wavelet transform, time-frequency localization and signal analysis

IEEE Transactions on Information Theory · 1990 · Vol. 36(5) · pp. 961–1005
Ingrid Daubechies

Abstract

Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Image and Signal Denoising MethodsBlind Source Separation TechniquesMachine Fault Diagnosis TechniquesTime–frequency analysisWaveletAlgorithmSIGNAL (programming language)Fourier transformWavelet transformStability (learning theory)Signal processingComputer scienceMathematics
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References
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Painless nonorthogonal expansions
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