Next Previous Contents

4. Pseudo-affine Wigner Transform

The pseudo-affine Wigner transform is only implemented for 1D signals. The organization is the same as for the continuous wavelet transform, with some minor variations. Tune first the fmin and fmax values, which are the minimum and maximum frequencies of analysis. The default values are the ones yielding maximal span compatible with the size of the signal. You may change the extreme frequencies either by typing values under fmin and fmax, or by using the predefined values on the menus to the right. Choose then the value of the parameter K from a choice of -1, 0, 0.5, 2 or enter your own choice. The Voices parameters governs the number of intermediate frequencies at which the continuous wavelet coefficients will be computed. Be warned that giving an excessive number of voices may result in large computing times for long signals. You may then decide to perform some Smoothing by entering a number in the corresponding box. Finally, you may choose the Size and Type of your analyzing wavelet: available wavelets are the Mexican Hat, and the real and analytic Morlet wavelets. The size may be any positive number (this parameter is not available for the Mexican hat). Once all the parameters are set, hit Compute. The output signal is a matrix of size "number of voices" x "size of the original signal". It is called paw_signal#. You may visualize the continuous wavelet transform using the View menu. Note that Fraclab recognizes pseudo-affine Wigner transforms, and display them differently form regular images. In particular, it uses a fixed aspect ratio (this is useful for instance if the number of voices is much smaller than the size of the signal), and the "jet" color-map, which often allows to highlight the important structures. If you want to view the transform as a normal image, or make other changes in the appearance, use the functionalities of the View menu described in the Overview help file.


Next Previous Contents