A new segmentation technique for noisy multi-component signals using wavelet transform

A new segmentation method of multi-component noisy signals using wavelet transform is proposed, when the signal components are closely spaced and the time interval between adjacent signal components are unknown. It is shown that Morlet wavelet transform is useful for segmenting a noisy signal, when the signal components are closely spaced. The segmentation problem is formulated using the paradigm of estimating the locations and durations of noisy narrow gaps of the input noisy signals. A wavelet scale sequence comprising of the highest absolute scales for each time instant is employed as test statistics for segmentation. A number of selected local maxima obtained from the wavelet scale sequence correspond to the position of the noisy gaps. Finally, windowed approximate entropy is calculated for the masked noisy signal to estimate the locations and durations of the narrow noisy gap as well as the noisy segments. The proposed scheme is evaluated on simulated examples.