| The aim of time-frequency analysis is to find a density in time and frequency that indicates which frequencies are present in the signal and how they evolve with time.In the general method of time-frequency analysis,wavelet analysis is internationaly recongnized up to the minute tools for analysing time-frequency.It is chiefly due to the "adaptive feature" and "mathematical microtelescope feature", wavelet analysis is becoming a focus point of many sciences, and is fondly delighted as tools for so many scientific workers. It plays an important tole in the signal & information processing.The point of sharp variation or singularities usually carry the important information about the signals. The advent of wavelet transform provides a suitable framework for studying the multiscale transient representation of signals. In mathematics, singularity can be described by lipschitz exponents. Mallat has proved that the lipschitz exponents can be obtained by calculating the decay of the local module maximum of the wavelet transform, which is the module maximum feature extraction methods based on wavelet transform.This paper summarizes briefly different time-frquency analysis techniques and research of time-frequency analysis. It discusses in detail the advantages and disadvantages of these techniques and their relations. Then the paper introduced entirely the basic theory of wavelet analysis and the module maximum. Feature extraction using wavelet transform, the most popularly, dyadic discrete wavelet transform has been successfully used in many research fields. In fact, Feature extraction using continuous wavelet transform has much more ability for quantitative description of singularity than of dyadic discrete wavelet transform. So in the paper, to periodic signal we suppose Morlet wavelet as basic wavelet, and research the local extreme point and extrema of the wavelet transform on periodic function for the collection of signal's instantaneous amplitude and period.
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