From the course: Wavelet Analysis: Concepts with Wolfram Language
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Continuous wavelet transform - Wolfram Language Tutorial
From the course: Wavelet Analysis: Concepts with Wolfram Language
Continuous wavelet transform
So now let us look at some of the basics of performing a continuous wavelet transform in Mathematica. Let us consider a simple sine function which is discretized over a certain range, as shown by this plot. When you take the data, which is given by the sine range of 40 divided by 5, and you perform a continuous wavelet transform on this, you get the result in the form of an object. This is because it contains a lot more additional information that users can access. The best way to represent and visualize the result that has been just done using the continuous wavelet transform is to make use of a wavelet scalogram. The scalogram represents the data in terms of octaves and voices. So when I evaluate this, you see that {1, 1}, {2, 1}, {3, 1}, {4, 1}. The first index is associated with the octave, second index is associated with a voice. Between two octaves, you'll see that there are four bands and that represents voices. So between each octave you have four voices. You can extract…
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