By adjusting the time-bandwidth product and symmetry parameters of a Morse wavelet, you can obtain analytic wavelets with different properties and behavior. A strength of Morse wavelets is that many commonly used analytic wavelets are special cases of a generalized Morse wavelet.

Choose a Wavelet. There are two types of wavelet analysis: continuous and multiresolution. The type of wavelet analysis best suited for your work depends on what you want to do with the data. This topic focuses on 1-D data, but you can apply the same principles to 2-D data.

To visualize in time other wavelets such as the Meyer, Morlet, Gaussian, Mexican hat, and Shannon wavelets, use wavefun. To display wavelet packets, use wpfun . To obtain more information on a wavelet family, use waveinfo .

By adjusting the time-bandwidth product and symmetry parameters of a Morse wavelet, you can obtain analytic wavelets with different properties and behavior. A strength of Morse wavelets is that many commonly used analytic wavelets are special cases of a generalized Morse wavelet.

wt = cwt(x) returns the continuous wavelet transform (CWT) of x. The CWT is obtained using the analytic Morse wavelet with the symmetry parameter, gamma (γ), equal to 3 and the time-bandwidth product equal to 60. cwt uses 10 voices per octave. The minimum and maximum scales are determined automatically based on the energy spread of the wavelet ...

The default wavelet used in the filter bank is the analytic Morse (3,60) wavelet. You can vary the time-bandwidth and symmetry parameters for the Morse wavelets, to tune the Morse wavelet for your needs. You can also use the analytic Morlet (Gabor) wavelet or bump wavelet.

[minfreq,maxfreq] = cwtfreqbounds(N) returns the minimum and maximum wavelet bandpass frequencies in cycles/sample for a signal of length N. The minimum and maximum frequencies are determined for the default Morse (3,60) wavelet.

Specify the Morse (5,20) wavelet. Visualize the scalogram using 26 voices per octave and periodic boundary extension. The frequencies are plotted on a logarithmic scale.

This example shows how to use wavelets to analyze physiologic signals. Physiologic signals are frequently nonstationary meaning that their frequency content changes over time. In many applications, these changes are the events of interest.

This example shows how to use the Wavelet Time-Frequency Analyzer app to visualize the scalogram of a 1-D signal. The scalogram is the absolute value of the continuous wavelet transform (CWT). You can adjust wavelet parameters, voices per octave, and frequency limits to use in the CWT.