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- Wavelet Scattering explanation? - Signal Processing Stack Exchange
Wavelet Scattering is an equivalent deep convolutional network, formed by cascade of wavelets, modulus nonlinearities, and lowpass filters It yields representations that are time-shift invariant, robust to noise, and stable against time-warping deformations - proving useful in many classification tasks and attaining SOTA on limited datasets Core results and intuition are provided in this
- PyWavelets CWT implementation - Signal Processing Stack Exchange
I seek to understand PyWavelets' implementation of the Continuous Wavelet Transform, and how it compares to the more 'basic' version I've coded and provided here In particular: How is integrated
- Wavelet thresholding - Signal Processing Stack Exchange
The soft thresholding is also called wavelet shrinkage, as values for both positive and negative coefficients are being "shrinked" towards zero, in contrary to hard thresholding which either keeps or removes values of coefficients In case of image de-noising, you are not working strictly on "intensity values", but wavelet coefficients
- cwt - Continuous Wavelet Transform vs Discrete Wavelet Transform . . .
The discrete wavelet transform is applied in many areas, such as signal compression, since it is easy to compute I notice that, However, the continuous wavelet transform (CWT) is also applied to
- Power Energy from Continuous Wavelet Transform
How can power or energy be computed from Continuous Wavelet Transform? Is it just $\sum |\text {CWT} (x)|^2$, or are there other considerations, particularly if interested in a subset of frequencies?
- Whats the difference between the Gabor and Morlet wavelets?
The Gabor wavelet is basically the same thing It's apparently another name for the Modified Morlet wavelet Quoting from : That book is a collection of papers, and that paper ("The Wavelet Transform and Time-Frequency Analysis") is by Leon Cohen (of time-frequency distribution "Cohen class" fame), so I think it's reasonably authoritative At the very least, it sounds like the confusion is
- python - Feature extraction reduction using DWT - Signal Processing . . .
For a given time series which is n timestamps in length, we can take Discrete Wavelet Transform (using 'Haar' wavelets), then we get (for an example, in Python) -
- interpret wavelet scalogram - Signal Processing Stack Exchange
My knowledge of wavelets is less than epsilon Bear with me If I have a signal of two well separated sinusoids (15 and 48 Hz) plus some random noise, I can clearly make out the two in a spectrogra
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