Abstract
Abstract The wavelet transform is of interest for analysing non-stationary signals. The squared modulus of the wavelet transform leads to the wavelet spectrogram or scalogram. When signals are embedded in additive noise, it is important to study the estimation accuracy in terms of bias and variance. The mean and variance statistical properties of the wavelet spectrogram of a signal embedded in additive gaussian white noise are derived in this paper. Examples and simulation results are also presented.
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